Improvise a Jazz Solo with an LSTM Network - v3.ipynb

Kernel: Python 3

Improvise a Jazz Solo with an LSTM Network

Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.

You will learn to:

• Apply an LSTM to music generation.

• Generate your own jazz music with deep learning.

Please run the following cell to load all the packages required in this assignment. This may take a few minutes.

from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import * 
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K

1 - Problem statement

You would like to create a jazz music piece specially for a friend's birthday. However, you don't know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM netwok.

You will train a network to generate novel jazz solos in a style representative of a body of performed work.

1.1 - Dataset

You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:

IPython.display.Audio('./data/30s_seq.mp3')

We have taken care of the preprocessing of the musical data to render it in terms of musical "values." You can informally think of each "value" as a note, which comprises a pitch and a duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a "value" is actually more complicated than this--specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playing multiple notes at the same time generates what's called a "chord"). But we don't need to worry about the details of music theory for this assignment. For the purpose of this assignment, all you need to know is that we will obtain a dataset of values, and will learn an RNN model to generate sequences of values.

Our music generation system will use 78 unique values. Run the following code to load the raw music data and preprocess it into values. This might take a few minutes.

X, Y, n_values, indices_values = load_music_utils()
print('shape of X:', X.shape)
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('Shape of Y:', Y.shape)

shape of X: (60, 30, 78)
number of training examples: 60
Tx (length of sequence): 30
total # of unique values: 78
Shape of Y: (30, 60, 78)

You have just loaded the following:

• X: This is an (m, $T_x$, 78) dimensional array. We have m training examples, each of which is a snippet of $T_x =30$ musical values. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t.

• Y: This is essentially the same as X, but shifted one step to the left (to the past). Similar to the dinosaurus assignment, we're interested in the network using the previous values to predict the next value, so our sequence model will try to predict $y^{\langle t \rangle}$ given $x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle}$. However, the data in Y is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed to the LSTM later.

• n_values: The number of unique values in this dataset. This should be 78.

• indices_values: python dictionary mapping from 0-77 to musical values.

1.2 - Overview of our model

Here is the architecture of the model we will use. This is similar to the Dinosaurus model you had used in the previous notebook, except that in you will be implementing it in Keras. The architecture is as follows:

We will be training the model on random snippets of 30 values taken from a much longer piece of music. Thus, we won't bother to set the first input $x^{\langle 1 \rangle} = \vec{0}$, which we had done previously to denote the start of a dinosaur name, since now most of these snippets of audio start somewhere in the middle of a piece of music. We are setting each of the snippts to have the same length $T_x = 30$ to make vectorization easier.

2 - Building the model

In this part you will build and train a model that will learn musical patterns. To do so, you will need to build a model that takes in X of shape $(m, T_x, 78)$ and Y of shape $(T_y, m, 78)$. We will use an LSTM with 64 dimensional hidden states. Lets set n_a = 64.

n_a = 64 

Here's how you can create a Keras model with multiple inputs and outputs. If you're building an RNN where even at test time entire input sequence $x^{\langle 1 \rangle}, x^{\langle 2 \rangle}, \ldots, x^{\langle T_x \rangle}$ were given in advance, for example if the inputs were words and the output was a label, then Keras has simple built-in functions to build the model. However, for sequence generation, at test time we don't know all the values of $x^{\langle t\rangle}$ in advance; instead we generate them one at a time using $x^{\langle t\rangle} = y^{\langle t-1 \rangle}$. So the code will be a bit more complicated, and you'll need to implement your own for-loop to iterate over the different time steps.

The function djmodel() will call the LSTM layer $T_x$ times using a for-loop, and it is important that all $T_x$ copies have the same weights. I.e., it should not re-initiaiize the weights every time---the $T_x$ steps should have shared weights. The key steps for implementing layers with shareable weights in Keras are:

1. Define the layer objects (we will use global variables for this).

2. Call these objects when propagating the input.

We have defined the layers objects you need as global variables. Please run the next cell to create them. Please check the Keras documentation to make sure you understand what these layers are: Reshape(), LSTM(), Dense().

reshapor = Reshape((1, 78))                        # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True)         # Used in Step 2.C
densor = Dense(n_values, activation='softmax')     # Used in Step 2.D

Each of reshapor, LSTM_cell and densor are now layer objects, and you can use them to implement djmodel(). In order to propagate a Keras tensor object X through one of these layers, use layer_object(X) (or layer_object([X,Y]) if it requires multiple inputs.). For example, reshapor(X) will propagate X through the Reshape((1,78)) layer defined above.

Exercise: Implement djmodel(). You will need to carry out 2 steps:

1. Create an empty list "outputs" to save the outputs of the LSTM Cell at every time step.

2. Loop for $t \in 1, \ldots, T_x$:

   A. Select the "t"th time-step vector from X. The shape of this selection should be (78,). To do so, create a custom Lambda layer in Keras by using this line of code:

           x = Lambda(lambda x: x[:,t,:])(X)

Look over the Keras documentation to figure out what this does. It is creating a "temporary" or "unnamed" function (that's what Lambda functions are) that extracts out the appropriate one-hot vector, and making this function a Keras Layer object to apply to X.

B. Reshape x to be (1,78). You may find the `reshapor()` layer (defined below) helpful.

C. Run x through one step of LSTM_cell. Remember to initialize the LSTM_cell with the previous step's hidden state $a$ and cell state $c$. Use the following formatting:

a, _, c = LSTM_cell(input_x, initial_state=[previous hidden state, previous cell state])

D. Propagate the LSTM's output activation value through a dense+softmax layer using `densor`. 

E. Append the predicted value to the list of "outputs"

# GRADED FUNCTION: djmodel

def djmodel(Tx, n_a, n_values):
    """
    Implement the model
    
    Arguments:
    Tx -- length of the sequence in a corpus
    n_a -- the number of activations used in our model
    n_values -- number of unique values in the music data 
    
    Returns:
    model -- a keras model with the 
    """
    
    # Define the input of your model with a shape 
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    
    ### START CODE HERE ### 
    # Step 1: Create empty list to append the outputs while you iterate (≈1 line)
    outputs = []
    
    # Step 2: Loop
    for t in range(Tx):
        
        # Step 2.A: select the "t"th time step vector from X. 
        x = Lambda(lambda x: X[:,t,:])(X)
        # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)
        x = reshapor(x)
        # Step 2.C: Perform one step of the LSTM_cell
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        # Step 2.D: Apply densor to the hidden state output of LSTM_Cell
        out = densor(a)
        # Step 2.E: add the output to "outputs"
        outputs.append(out)
        
    # Step 3: Create model instance
    model = Model(inputs=[X, a0, c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return model

Run the following cell to define your model. We will use Tx=30, n_a=64 (the dimension of the LSTM activations), and n_values=78. This cell may take a few seconds to run.

model = djmodel(Tx = 30 , n_a = 64, n_values = 78)

You now need to compile your model to be trained. We will Adam and a categorical cross-entropy loss.

opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)

model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

Finally, lets initialize a0 and c0 for the LSTM's initial state to be zero.

m = 60
a0 = np.zeros((m, n_a))
c0 = np.zeros((m, n_a))

Lets now fit the model! We will turn Y to a list before doing so, since the cost function expects Y to be provided in this format (one list item per time-step). So list(Y) is a list with 30 items, where each of the list items is of shape (60,78). Lets train for 100 epochs. This will take a few minutes.

model.fit([X, a0, c0], list(Y), epochs=100)

Epoch 1/100
60/60 [==============================] - 5s - loss: 126.1118 - dense_3_loss_1: 4.3553 - dense_3_loss_2: 4.3513 - dense_3_loss_3: 4.3464 - dense_3_loss_4: 4.3535 - dense_3_loss_5: 4.3584 - dense_3_loss_6: 4.3442 - dense_3_loss_7: 4.3522 - dense_3_loss_8: 4.3429 - dense_3_loss_9: 4.3560 - dense_3_loss_10: 4.3445 - dense_3_loss_11: 4.3449 - dense_3_loss_12: 4.3547 - dense_3_loss_13: 4.3433 - dense_3_loss_14: 4.3404 - dense_3_loss_15: 4.3485 - dense_3_loss_16: 4.3480 - dense_3_loss_17: 4.3516 - dense_3_loss_18: 4.3449 - dense_3_loss_19: 4.3530 - dense_3_loss_20: 4.3497 - dense_3_loss_21: 4.3491 - dense_3_loss_22: 4.3467 - dense_3_loss_23: 4.3407 - dense_3_loss_24: 4.3408 - dense_3_loss_25: 4.3512 - dense_3_loss_26: 4.3508 - dense_3_loss_27: 4.3498 - dense_3_loss_28: 4.3467 - dense_3_loss_29: 4.3524 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0333 - dense_3_acc_2: 0.0167 - dense_3_acc_3: 0.1000 - dense_3_acc_4: 0.0167 - dense_3_acc_5: 0.0000e+00 - dense_3_acc_6: 0.0333 - dense_3_acc_7: 0.0333 - dense_3_acc_8: 0.0500 - dense_3_acc_9: 0.0000e+00 - dense_3_acc_10: 0.0167 - dense_3_acc_11: 0.0167 - dense_3_acc_12: 0.0167 - dense_3_acc_13: 0.0333 - dense_3_acc_14: 0.0333 - dense_3_acc_15: 0.0500 - dense_3_acc_16: 0.0333 - dense_3_acc_17: 0.0333 - dense_3_acc_18: 0.0667 - dense_3_acc_19: 0.0500 - dense_3_acc_20: 0.0500 - dense_3_acc_21: 0.0000e+00 - dense_3_acc_22: 0.0667 - dense_3_acc_23: 0.0333 - dense_3_acc_24: 0.0500 - dense_3_acc_25: 0.0167 - dense_3_acc_26: 0.0667 - dense_3_acc_27: 0.0333 - dense_3_acc_28: 0.0500 - dense_3_acc_29: 0.0167 - dense_3_acc_30: 0.0167                                                                                                     
Epoch 2/100
60/60 [==============================] - 0s - loss: 123.8246 - dense_3_loss_1: 4.3374 - dense_3_loss_2: 4.3140 - dense_3_loss_3: 4.2923 - dense_3_loss_4: 4.2995 - dense_3_loss_5: 4.2879 - dense_3_loss_6: 4.2764 - dense_3_loss_7: 4.2785 - dense_3_loss_8: 4.2571 - dense_3_loss_9: 4.2812 - dense_3_loss_10: 4.2605 - dense_3_loss_11: 4.2484 - dense_3_loss_12: 4.2836 - dense_3_loss_13: 4.2546 - dense_3_loss_14: 4.2459 - dense_3_loss_15: 4.2512 - dense_3_loss_16: 4.2710 - dense_3_loss_17: 4.2582 - dense_3_loss_18: 4.2655 - dense_3_loss_19: 4.2533 - dense_3_loss_20: 4.2703 - dense_3_loss_21: 4.2647 - dense_3_loss_22: 4.2503 - dense_3_loss_23: 4.2626 - dense_3_loss_24: 4.2607 - dense_3_loss_25: 4.2662 - dense_3_loss_26: 4.2455 - dense_3_loss_27: 4.2630 - dense_3_loss_28: 4.2601 - dense_3_loss_29: 4.2650 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1333 - dense_3_acc_3: 0.3167 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2333 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1500 - dense_3_acc_8: 0.2000 - dense_3_acc_9: 0.1167 - dense_3_acc_10: 0.2000 - dense_3_acc_11: 0.2000 - dense_3_acc_12: 0.1000 - dense_3_acc_13: 0.2167 - dense_3_acc_14: 0.1667 - dense_3_acc_15: 0.2167 - dense_3_acc_16: 0.1667 - dense_3_acc_17: 0.1833 - dense_3_acc_18: 0.1000 - dense_3_acc_19: 0.2500 - dense_3_acc_20: 0.1000 - dense_3_acc_21: 0.0833 - dense_3_acc_22: 0.2000 - dense_3_acc_23: 0.1833 - dense_3_acc_24: 0.1833 - dense_3_acc_25: 0.0833 - dense_3_acc_26: 0.2167 - dense_3_acc_27: 0.1000 - dense_3_acc_28: 0.1833 - dense_3_acc_29: 0.1333 - dense_3_acc_30: 0.0000e+00     
Epoch 3/100
60/60 [==============================] - 0s - loss: 118.3728 - dense_3_loss_1: 4.3172 - dense_3_loss_2: 4.2651 - dense_3_loss_3: 4.2179 - dense_3_loss_4: 4.2153 - dense_3_loss_5: 4.1873 - dense_3_loss_6: 4.1726 - dense_3_loss_7: 4.1569 - dense_3_loss_8: 4.1027 - dense_3_loss_9: 4.1188 - dense_3_loss_10: 4.0480 - dense_3_loss_11: 4.0060 - dense_3_loss_12: 4.1426 - dense_3_loss_13: 4.0462 - dense_3_loss_14: 3.9892 - dense_3_loss_15: 4.0128 - dense_3_loss_16: 4.1018 - dense_3_loss_17: 4.0384 - dense_3_loss_18: 4.0969 - dense_3_loss_19: 3.9223 - dense_3_loss_20: 4.0688 - dense_3_loss_21: 4.0272 - dense_3_loss_22: 3.9508 - dense_3_loss_23: 4.0037 - dense_3_loss_24: 4.0295 - dense_3_loss_25: 4.0776 - dense_3_loss_26: 3.8831 - dense_3_loss_27: 4.0490 - dense_3_loss_28: 4.0227 - dense_3_loss_29: 4.1025 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1833 - dense_3_acc_3: 0.2667 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2333 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1667 - dense_3_acc_8: 0.2000 - dense_3_acc_9: 0.1333 - dense_3_acc_10: 0.1833 - dense_3_acc_11: 0.2500 - dense_3_acc_12: 0.1167 - dense_3_acc_13: 0.1833 - dense_3_acc_14: 0.1833 - dense_3_acc_15: 0.2000 - dense_3_acc_16: 0.1500 - dense_3_acc_17: 0.1333 - dense_3_acc_18: 0.0833 - dense_3_acc_19: 0.1500 - dense_3_acc_20: 0.0833 - dense_3_acc_21: 0.0667 - dense_3_acc_22: 0.1333 - dense_3_acc_23: 0.1167 - dense_3_acc_24: 0.1167 - dense_3_acc_25: 0.0333 - dense_3_acc_26: 0.1500 - dense_3_acc_27: 0.0833 - dense_3_acc_28: 0.0833 - dense_3_acc_29: 0.0500 - dense_3_acc_30: 0.0000e+00     
Epoch 4/100
60/60 [==============================] - 0s - loss: 113.7108 - dense_3_loss_1: 4.2938 - dense_3_loss_2: 4.2134 - dense_3_loss_3: 4.1228 - dense_3_loss_4: 4.1085 - dense_3_loss_5: 4.0300 - dense_3_loss_6: 4.0191 - dense_3_loss_7: 3.9831 - dense_3_loss_8: 3.8023 - dense_3_loss_9: 3.8882 - dense_3_loss_10: 3.7468 - dense_3_loss_11: 3.7368 - dense_3_loss_12: 4.0171 - dense_3_loss_13: 3.7977 - dense_3_loss_14: 3.7457 - dense_3_loss_15: 3.7887 - dense_3_loss_16: 3.8815 - dense_3_loss_17: 3.8859 - dense_3_loss_18: 3.9349 - dense_3_loss_19: 3.7242 - dense_3_loss_20: 4.0906 - dense_3_loss_21: 3.9682 - dense_3_loss_22: 3.8443 - dense_3_loss_23: 3.8724 - dense_3_loss_24: 3.8041 - dense_3_loss_25: 4.0307 - dense_3_loss_26: 3.5840 - dense_3_loss_27: 3.7719 - dense_3_loss_28: 3.9174 - dense_3_loss_29: 4.1068 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1167 - dense_3_acc_3: 0.2667 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2333 - dense_3_acc_6: 0.1667 - dense_3_acc_7: 0.1500 - dense_3_acc_8: 0.2500 - dense_3_acc_9: 0.1667 - dense_3_acc_10: 0.1667 - dense_3_acc_11: 0.1500 - dense_3_acc_12: 0.1167 - dense_3_acc_13: 0.1500 - dense_3_acc_14: 0.1167 - dense_3_acc_15: 0.1000 - dense_3_acc_16: 0.1500 - dense_3_acc_17: 0.2167 - dense_3_acc_18: 0.1000 - dense_3_acc_19: 0.1167 - dense_3_acc_20: 0.1167 - dense_3_acc_21: 0.1167 - dense_3_acc_22: 0.0500 - dense_3_acc_23: 0.0833 - dense_3_acc_24: 0.0667 - dense_3_acc_25: 0.0667 - dense_3_acc_26: 0.2000 - dense_3_acc_27: 0.0333 - dense_3_acc_28: 0.1500 - dense_3_acc_29: 0.1167 - dense_3_acc_30: 0.0000e+00     
Epoch 5/100
60/60 [==============================] - 0s - loss: 110.0209 - dense_3_loss_1: 4.2753 - dense_3_loss_2: 4.1686 - dense_3_loss_3: 4.0497 - dense_3_loss_4: 4.0376 - dense_3_loss_5: 3.9272 - dense_3_loss_6: 3.9271 - dense_3_loss_7: 3.8912 - dense_3_loss_8: 3.6758 - dense_3_loss_9: 3.7692 - dense_3_loss_10: 3.6226 - dense_3_loss_11: 3.6503 - dense_3_loss_12: 3.9224 - dense_3_loss_13: 3.6801 - dense_3_loss_14: 3.5979 - dense_3_loss_15: 3.6682 - dense_3_loss_16: 3.6671 - dense_3_loss_17: 3.7602 - dense_3_loss_18: 3.7393 - dense_3_loss_19: 3.6160 - dense_3_loss_20: 3.8432 - dense_3_loss_21: 3.8308 - dense_3_loss_22: 3.7170 - dense_3_loss_23: 3.6743 - dense_3_loss_24: 3.6484 - dense_3_loss_25: 3.8495 - dense_3_loss_26: 3.4997 - dense_3_loss_27: 3.6349 - dense_3_loss_28: 3.7260 - dense_3_loss_29: 3.9515 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.0833 - dense_3_acc_3: 0.2833 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2333 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1333 - dense_3_acc_8: 0.2667 - dense_3_acc_9: 0.1500 - dense_3_acc_10: 0.1500 - dense_3_acc_11: 0.1667 - dense_3_acc_12: 0.1333 - dense_3_acc_13: 0.2000 - dense_3_acc_14: 0.2167 - dense_3_acc_15: 0.1833 - dense_3_acc_16: 0.1333 - dense_3_acc_17: 0.1833 - dense_3_acc_18: 0.1167 - dense_3_acc_19: 0.1167 - dense_3_acc_20: 0.0667 - dense_3_acc_21: 0.0333 - dense_3_acc_22: 0.1333 - dense_3_acc_23: 0.0833 - dense_3_acc_24: 0.0833 - dense_3_acc_25: 0.1167 - dense_3_acc_26: 0.1500 - dense_3_acc_27: 0.0833 - dense_3_acc_28: 0.1333 - dense_3_acc_29: 0.0833 - dense_3_acc_30: 0.0000e+00             
Epoch 6/100
60/60 [==============================] - 0s - loss: 107.8434 - dense_3_loss_1: 4.2583 - dense_3_loss_2: 4.1304 - dense_3_loss_3: 3.9784 - dense_3_loss_4: 3.9707 - dense_3_loss_5: 3.8547 - dense_3_loss_6: 3.8544 - dense_3_loss_7: 3.7905 - dense_3_loss_8: 3.5720 - dense_3_loss_9: 3.6601 - dense_3_loss_10: 3.5044 - dense_3_loss_11: 3.5828 - dense_3_loss_12: 3.7893 - dense_3_loss_13: 3.5590 - dense_3_loss_14: 3.4282 - dense_3_loss_15: 3.5668 - dense_3_loss_16: 3.5629 - dense_3_loss_17: 3.6235 - dense_3_loss_18: 3.6196 - dense_3_loss_19: 3.6181 - dense_3_loss_20: 3.7039 - dense_3_loss_21: 3.8089 - dense_3_loss_22: 3.6692 - dense_3_loss_23: 3.6665 - dense_3_loss_24: 3.6602 - dense_3_loss_25: 3.7754 - dense_3_loss_26: 3.5163 - dense_3_loss_27: 3.7160 - dense_3_loss_28: 3.5959 - dense_3_loss_29: 3.8069 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1333 - dense_3_acc_3: 0.2167 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2167 - dense_3_acc_6: 0.1167 - dense_3_acc_7: 0.1500 - dense_3_acc_8: 0.2167 - dense_3_acc_9: 0.1333 - dense_3_acc_10: 0.1500 - dense_3_acc_11: 0.1667 - dense_3_acc_12: 0.1333 - dense_3_acc_13: 0.1333 - dense_3_acc_14: 0.1667 - dense_3_acc_15: 0.1167 - dense_3_acc_16: 0.1667 - dense_3_acc_17: 0.1667 - dense_3_acc_18: 0.1000 - dense_3_acc_19: 0.1667 - dense_3_acc_20: 0.1000 - dense_3_acc_21: 0.1000 - dense_3_acc_22: 0.1167 - dense_3_acc_23: 0.0333 - dense_3_acc_24: 0.0167 - dense_3_acc_25: 0.0833 - dense_3_acc_26: 0.1167 - dense_3_acc_27: 0.0667 - dense_3_acc_28: 0.1500 - dense_3_acc_29: 0.1000 - dense_3_acc_30: 0.0000e+00         
Epoch 7/100
60/60 [==============================] - 0s - loss: 104.1943 - dense_3_loss_1: 4.2418 - dense_3_loss_2: 4.0958 - dense_3_loss_3: 3.9148 - dense_3_loss_4: 3.9069 - dense_3_loss_5: 3.7607 - dense_3_loss_6: 3.7842 - dense_3_loss_7: 3.7085 - dense_3_loss_8: 3.4612 - dense_3_loss_9: 3.5298 - dense_3_loss_10: 3.3491 - dense_3_loss_11: 3.4849 - dense_3_loss_12: 3.6534 - dense_3_loss_13: 3.3752 - dense_3_loss_14: 3.3222 - dense_3_loss_15: 3.4568 - dense_3_loss_16: 3.3998 - dense_3_loss_17: 3.4614 - dense_3_loss_18: 3.5196 - dense_3_loss_19: 3.3982 - dense_3_loss_20: 3.5393 - dense_3_loss_21: 3.6235 - dense_3_loss_22: 3.5334 - dense_3_loss_23: 3.5137 - dense_3_loss_24: 3.4900 - dense_3_loss_25: 3.6681 - dense_3_loss_26: 3.3889 - dense_3_loss_27: 3.5190 - dense_3_loss_28: 3.4495 - dense_3_loss_29: 3.6446 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1333 - dense_3_acc_3: 0.1833 - dense_3_acc_4: 0.1500 - dense_3_acc_5: 0.2167 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1333 - dense_3_acc_8: 0.2000 - dense_3_acc_9: 0.1000 - dense_3_acc_10: 0.1500 - dense_3_acc_11: 0.1500 - dense_3_acc_12: 0.0833 - dense_3_acc_13: 0.1167 - dense_3_acc_14: 0.1667 - dense_3_acc_15: 0.1333 - dense_3_acc_16: 0.1667 - dense_3_acc_17: 0.1833 - dense_3_acc_18: 0.1000 - dense_3_acc_19: 0.1667 - dense_3_acc_20: 0.1167 - dense_3_acc_21: 0.1500 - dense_3_acc_22: 0.1167 - dense_3_acc_23: 0.1333 - dense_3_acc_24: 0.0833 - dense_3_acc_25: 0.0833 - dense_3_acc_26: 0.2000 - dense_3_acc_27: 0.0833 - dense_3_acc_28: 0.1167 - dense_3_acc_29: 0.1167 - dense_3_acc_30: 0.0000e+00     
Epoch 8/100
60/60 [==============================] - 0s - loss: 101.6762 - dense_3_loss_1: 4.2270 - dense_3_loss_2: 4.0575 - dense_3_loss_3: 3.8506 - dense_3_loss_4: 3.8327 - dense_3_loss_5: 3.6756 - dense_3_loss_6: 3.7003 - dense_3_loss_7: 3.6136 - dense_3_loss_8: 3.3488 - dense_3_loss_9: 3.4066 - dense_3_loss_10: 3.2426 - dense_3_loss_11: 3.4190 - dense_3_loss_12: 3.5699 - dense_3_loss_13: 3.3028 - dense_3_loss_14: 3.2710 - dense_3_loss_15: 3.3482 - dense_3_loss_16: 3.3396 - dense_3_loss_17: 3.4042 - dense_3_loss_18: 3.4629 - dense_3_loss_19: 3.3750 - dense_3_loss_20: 3.4619 - dense_3_loss_21: 3.5393 - dense_3_loss_22: 3.4436 - dense_3_loss_23: 3.4314 - dense_3_loss_24: 3.3409 - dense_3_loss_25: 3.5509 - dense_3_loss_26: 3.2300 - dense_3_loss_27: 3.3950 - dense_3_loss_28: 3.3760 - dense_3_loss_29: 3.4592 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.1333 - dense_3_acc_3: 0.2000 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2167 - dense_3_acc_6: 0.1167 - dense_3_acc_7: 0.1167 - dense_3_acc_8: 0.2333 - dense_3_acc_9: 0.1000 - dense_3_acc_10: 0.1167 - dense_3_acc_11: 0.1167 - dense_3_acc_12: 0.0667 - dense_3_acc_13: 0.1333 - dense_3_acc_14: 0.1500 - dense_3_acc_15: 0.1167 - dense_3_acc_16: 0.1167 - dense_3_acc_17: 0.1833 - dense_3_acc_18: 0.1167 - dense_3_acc_19: 0.1333 - dense_3_acc_20: 0.1333 - dense_3_acc_21: 0.1500 - dense_3_acc_22: 0.0833 - dense_3_acc_23: 0.1333 - dense_3_acc_24: 0.1000 - dense_3_acc_25: 0.1000 - dense_3_acc_26: 0.2000 - dense_3_acc_27: 0.0667 - dense_3_acc_28: 0.1667 - dense_3_acc_29: 0.1333 - dense_3_acc_30: 0.0000e+00     
Epoch 9/100
60/60 [==============================] - 0s - loss: 97.8955 - dense_3_loss_1: 4.2156 - dense_3_loss_2: 4.0233 - dense_3_loss_3: 3.7861 - dense_3_loss_4: 3.7710 - dense_3_loss_5: 3.5925 - dense_3_loss_6: 3.6228 - dense_3_loss_7: 3.5265 - dense_3_loss_8: 3.2317 - dense_3_loss_9: 3.3197 - dense_3_loss_10: 3.1262 - dense_3_loss_11: 3.2999 - dense_3_loss_12: 3.4279 - dense_3_loss_13: 3.1580 - dense_3_loss_14: 3.0937 - dense_3_loss_15: 3.2028 - dense_3_loss_16: 3.2803 - dense_3_loss_17: 3.2478 - dense_3_loss_18: 3.3265 - dense_3_loss_19: 3.1340 - dense_3_loss_20: 3.3169 - dense_3_loss_21: 3.3388 - dense_3_loss_22: 3.2082 - dense_3_loss_23: 3.2373 - dense_3_loss_24: 3.1836 - dense_3_loss_25: 3.4106 - dense_3_loss_26: 3.0578 - dense_3_loss_27: 3.3331 - dense_3_loss_28: 3.1140 - dense_3_loss_29: 3.3090 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2167 - dense_3_acc_3: 0.2333 - dense_3_acc_4: 0.1667 - dense_3_acc_5: 0.2167 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1667 - dense_3_acc_8: 0.3000 - dense_3_acc_9: 0.1333 - dense_3_acc_10: 0.1333 - dense_3_acc_11: 0.1667 - dense_3_acc_12: 0.1333 - dense_3_acc_13: 0.1500 - dense_3_acc_14: 0.1833 - dense_3_acc_15: 0.1500 - dense_3_acc_16: 0.1500 - dense_3_acc_17: 0.1833 - dense_3_acc_18: 0.1167 - dense_3_acc_19: 0.1667 - dense_3_acc_20: 0.1667 - dense_3_acc_21: 0.1333 - dense_3_acc_22: 0.1333 - dense_3_acc_23: 0.1500 - dense_3_acc_24: 0.1000 - dense_3_acc_25: 0.1833 - dense_3_acc_26: 0.2500 - dense_3_acc_27: 0.1000 - dense_3_acc_28: 0.2000 - dense_3_acc_29: 0.1333 - dense_3_acc_30: 0.0000e+00     
Epoch 10/100
60/60 [==============================] - 0s - loss: 94.2230 - dense_3_loss_1: 4.2040 - dense_3_loss_2: 3.9859 - dense_3_loss_3: 3.7216 - dense_3_loss_4: 3.6901 - dense_3_loss_5: 3.4984 - dense_3_loss_6: 3.5200 - dense_3_loss_7: 3.4265 - dense_3_loss_8: 3.1216 - dense_3_loss_9: 3.1892 - dense_3_loss_10: 2.9889 - dense_3_loss_11: 3.1914 - dense_3_loss_12: 3.2701 - dense_3_loss_13: 3.0272 - dense_3_loss_14: 2.9346 - dense_3_loss_15: 3.0405 - dense_3_loss_16: 3.1474 - dense_3_loss_17: 3.0923 - dense_3_loss_18: 3.1408 - dense_3_loss_19: 2.9630 - dense_3_loss_20: 3.1321 - dense_3_loss_21: 3.1900 - dense_3_loss_22: 3.0592 - dense_3_loss_23: 3.1198 - dense_3_loss_24: 3.0060 - dense_3_loss_25: 3.3227 - dense_3_loss_26: 2.8878 - dense_3_loss_27: 3.1774 - dense_3_loss_28: 2.9969 - dense_3_loss_29: 3.1779 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.2333 - dense_3_acc_4: 0.1833 - dense_3_acc_5: 0.2167 - dense_3_acc_6: 0.1167 - dense_3_acc_7: 0.1500 - dense_3_acc_8: 0.3000 - dense_3_acc_9: 0.1833 - dense_3_acc_10: 0.2167 - dense_3_acc_11: 0.2000 - dense_3_acc_12: 0.1833 - dense_3_acc_13: 0.2000 - dense_3_acc_14: 0.2500 - dense_3_acc_15: 0.2833 - dense_3_acc_16: 0.1833 - dense_3_acc_17: 0.2333 - dense_3_acc_18: 0.1333 - dense_3_acc_19: 0.2167 - dense_3_acc_20: 0.2000 - dense_3_acc_21: 0.1500 - dense_3_acc_22: 0.1500 - dense_3_acc_23: 0.1333 - dense_3_acc_24: 0.1167 - dense_3_acc_25: 0.1833 - dense_3_acc_26: 0.3000 - dense_3_acc_27: 0.1333 - dense_3_acc_28: 0.2833 - dense_3_acc_29: 0.1833 - dense_3_acc_30: 0.0000e+00     
Epoch 11/100
60/60 [==============================] - 0s - loss: 90.4854 - dense_3_loss_1: 4.1923 - dense_3_loss_2: 3.9451 - dense_3_loss_3: 3.6519 - dense_3_loss_4: 3.5996 - dense_3_loss_5: 3.3944 - dense_3_loss_6: 3.3852 - dense_3_loss_7: 3.2951 - dense_3_loss_8: 2.9966 - dense_3_loss_9: 3.0208 - dense_3_loss_10: 2.8121 - dense_3_loss_11: 3.0633 - dense_3_loss_12: 3.1206 - dense_3_loss_13: 2.8604 - dense_3_loss_14: 2.8030 - dense_3_loss_15: 2.8735 - dense_3_loss_16: 2.9903 - dense_3_loss_17: 2.9067 - dense_3_loss_18: 2.9812 - dense_3_loss_19: 2.8665 - dense_3_loss_20: 2.9703 - dense_3_loss_21: 3.0456 - dense_3_loss_22: 2.9407 - dense_3_loss_23: 3.0562 - dense_3_loss_24: 2.8456 - dense_3_loss_25: 3.2281 - dense_3_loss_26: 2.7603 - dense_3_loss_27: 2.9909 - dense_3_loss_28: 2.8796 - dense_3_loss_29: 3.0097 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.2500 - dense_3_acc_4: 0.1833 - dense_3_acc_5: 0.2333 - dense_3_acc_6: 0.1167 - dense_3_acc_7: 0.1833 - dense_3_acc_8: 0.2833 - dense_3_acc_9: 0.2000 - dense_3_acc_10: 0.2500 - dense_3_acc_11: 0.2167 - dense_3_acc_12: 0.1667 - dense_3_acc_13: 0.2833 - dense_3_acc_14: 0.2667 - dense_3_acc_15: 0.2167 - dense_3_acc_16: 0.2000 - dense_3_acc_17: 0.2667 - dense_3_acc_18: 0.1667 - dense_3_acc_19: 0.2500 - dense_3_acc_20: 0.2500 - dense_3_acc_21: 0.1667 - dense_3_acc_22: 0.2167 - dense_3_acc_23: 0.1833 - dense_3_acc_24: 0.1667 - dense_3_acc_25: 0.1667 - dense_3_acc_26: 0.3500 - dense_3_acc_27: 0.1833 - dense_3_acc_28: 0.2667 - dense_3_acc_29: 0.2333 - dense_3_acc_30: 0.0000e+00     
Epoch 12/100
60/60 [==============================] - 0s - loss: 86.3358 - dense_3_loss_1: 4.1813 - dense_3_loss_2: 3.9037 - dense_3_loss_3: 3.5705 - dense_3_loss_4: 3.5033 - dense_3_loss_5: 3.2665 - dense_3_loss_6: 3.2403 - dense_3_loss_7: 3.1624 - dense_3_loss_8: 2.8482 - dense_3_loss_9: 2.8673 - dense_3_loss_10: 2.6542 - dense_3_loss_11: 2.9265 - dense_3_loss_12: 2.9449 - dense_3_loss_13: 2.6931 - dense_3_loss_14: 2.6534 - dense_3_loss_15: 2.7400 - dense_3_loss_16: 2.8594 - dense_3_loss_17: 2.7406 - dense_3_loss_18: 2.8070 - dense_3_loss_19: 2.7449 - dense_3_loss_20: 2.7584 - dense_3_loss_21: 2.8770 - dense_3_loss_22: 2.7960 - dense_3_loss_23: 2.8768 - dense_3_loss_24: 2.6823 - dense_3_loss_25: 3.0603 - dense_3_loss_26: 2.6122 - dense_3_loss_27: 2.8280 - dense_3_loss_28: 2.6888 - dense_3_loss_29: 2.8486 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.2667 - dense_3_acc_4: 0.2000 - dense_3_acc_5: 0.2667 - dense_3_acc_6: 0.1333 - dense_3_acc_7: 0.1833 - dense_3_acc_8: 0.3167 - dense_3_acc_9: 0.2333 - dense_3_acc_10: 0.3000 - dense_3_acc_11: 0.2167 - dense_3_acc_12: 0.1667 - dense_3_acc_13: 0.3000 - dense_3_acc_14: 0.2667 - dense_3_acc_15: 0.2833 - dense_3_acc_16: 0.2167 - dense_3_acc_17: 0.2667 - dense_3_acc_18: 0.1833 - dense_3_acc_19: 0.2500 - dense_3_acc_20: 0.2833 - dense_3_acc_21: 0.2000 - dense_3_acc_22: 0.2167 - dense_3_acc_23: 0.2000 - dense_3_acc_24: 0.2167 - dense_3_acc_25: 0.1667 - dense_3_acc_26: 0.3500 - dense_3_acc_27: 0.2333 - dense_3_acc_28: 0.2333 - dense_3_acc_29: 0.2333 - dense_3_acc_30: 0.0000e+00     
Epoch 13/100
60/60 [==============================] - 0s - loss: 81.8622 - dense_3_loss_1: 4.1699 - dense_3_loss_2: 3.8613 - dense_3_loss_3: 3.4909 - dense_3_loss_4: 3.4042 - dense_3_loss_5: 3.1406 - dense_3_loss_6: 3.0816 - dense_3_loss_7: 3.0131 - dense_3_loss_8: 2.6724 - dense_3_loss_9: 2.7285 - dense_3_loss_10: 2.5282 - dense_3_loss_11: 2.7830 - dense_3_loss_12: 2.7362 - dense_3_loss_13: 2.5601 - dense_3_loss_14: 2.5064 - dense_3_loss_15: 2.6074 - dense_3_loss_16: 2.7094 - dense_3_loss_17: 2.6031 - dense_3_loss_18: 2.6107 - dense_3_loss_19: 2.5497 - dense_3_loss_20: 2.5545 - dense_3_loss_21: 2.6475 - dense_3_loss_22: 2.6139 - dense_3_loss_23: 2.6596 - dense_3_loss_24: 2.5268 - dense_3_loss_25: 2.8750 - dense_3_loss_26: 2.4060 - dense_3_loss_27: 2.6113 - dense_3_loss_28: 2.4921 - dense_3_loss_29: 2.7184 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.2667 - dense_3_acc_4: 0.2000 - dense_3_acc_5: 0.2667 - dense_3_acc_6: 0.2167 - dense_3_acc_7: 0.2500 - dense_3_acc_8: 0.3833 - dense_3_acc_9: 0.2500 - dense_3_acc_10: 0.3167 - dense_3_acc_11: 0.1833 - dense_3_acc_12: 0.2333 - dense_3_acc_13: 0.3667 - dense_3_acc_14: 0.2833 - dense_3_acc_15: 0.3167 - dense_3_acc_16: 0.2333 - dense_3_acc_17: 0.2667 - dense_3_acc_18: 0.2333 - dense_3_acc_19: 0.2500 - dense_3_acc_20: 0.3000 - dense_3_acc_21: 0.3167 - dense_3_acc_22: 0.1333 - dense_3_acc_23: 0.2667 - dense_3_acc_24: 0.2167 - dense_3_acc_25: 0.2000 - dense_3_acc_26: 0.3667 - dense_3_acc_27: 0.3500 - dense_3_acc_28: 0.3500 - dense_3_acc_29: 0.1667 - dense_3_acc_30: 0.0000e+00     
Epoch 14/100
60/60 [==============================] - 0s - loss: 77.6850 - dense_3_loss_1: 4.1625 - dense_3_loss_2: 3.8199 - dense_3_loss_3: 3.4092 - dense_3_loss_4: 3.2978 - dense_3_loss_5: 3.0090 - dense_3_loss_6: 2.9303 - dense_3_loss_7: 2.8663 - dense_3_loss_8: 2.5155 - dense_3_loss_9: 2.6159 - dense_3_loss_10: 2.4199 - dense_3_loss_11: 2.6435 - dense_3_loss_12: 2.5479 - dense_3_loss_13: 2.4211 - dense_3_loss_14: 2.3778 - dense_3_loss_15: 2.4619 - dense_3_loss_16: 2.5701 - dense_3_loss_17: 2.4543 - dense_3_loss_18: 2.4428 - dense_3_loss_19: 2.3684 - dense_3_loss_20: 2.3608 - dense_3_loss_21: 2.4635 - dense_3_loss_22: 2.4357 - dense_3_loss_23: 2.4661 - dense_3_loss_24: 2.3819 - dense_3_loss_25: 2.6491 - dense_3_loss_26: 2.2543 - dense_3_loss_27: 2.4507 - dense_3_loss_28: 2.3213 - dense_3_loss_29: 2.5676 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.3167 - dense_3_acc_4: 0.2000 - dense_3_acc_5: 0.3000 - dense_3_acc_6: 0.2000 - dense_3_acc_7: 0.3000 - dense_3_acc_8: 0.3833 - dense_3_acc_9: 0.2333 - dense_3_acc_10: 0.3667 - dense_3_acc_11: 0.2667 - dense_3_acc_12: 0.3000 - dense_3_acc_13: 0.3500 - dense_3_acc_14: 0.3333 - dense_3_acc_15: 0.4333 - dense_3_acc_16: 0.3167 - dense_3_acc_17: 0.2833 - dense_3_acc_18: 0.2833 - dense_3_acc_19: 0.3167 - dense_3_acc_20: 0.3833 - dense_3_acc_21: 0.3833 - dense_3_acc_22: 0.2333 - dense_3_acc_23: 0.3167 - dense_3_acc_24: 0.2833 - dense_3_acc_25: 0.1667 - dense_3_acc_26: 0.4000 - dense_3_acc_27: 0.4500 - dense_3_acc_28: 0.4000 - dense_3_acc_29: 0.2667 - dense_3_acc_30: 0.0000e+00     
Epoch 15/100
60/60 [==============================] - 0s - loss: 73.4183 - dense_3_loss_1: 4.1542 - dense_3_loss_2: 3.7763 - dense_3_loss_3: 3.3231 - dense_3_loss_4: 3.1951 - dense_3_loss_5: 2.8719 - dense_3_loss_6: 2.7722 - dense_3_loss_7: 2.7070 - dense_3_loss_8: 2.3727 - dense_3_loss_9: 2.4859 - dense_3_loss_10: 2.2607 - dense_3_loss_11: 2.4655 - dense_3_loss_12: 2.3935 - dense_3_loss_13: 2.3033 - dense_3_loss_14: 2.2042 - dense_3_loss_15: 2.3261 - dense_3_loss_16: 2.4050 - dense_3_loss_17: 2.2352 - dense_3_loss_18: 2.3147 - dense_3_loss_19: 2.2320 - dense_3_loss_20: 2.2005 - dense_3_loss_21: 2.2473 - dense_3_loss_22: 2.2415 - dense_3_loss_23: 2.2959 - dense_3_loss_24: 2.1531 - dense_3_loss_25: 2.4651 - dense_3_loss_26: 2.0410 - dense_3_loss_27: 2.3257 - dense_3_loss_28: 2.2369 - dense_3_loss_29: 2.4129 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.3333 - dense_3_acc_4: 0.2333 - dense_3_acc_5: 0.3500 - dense_3_acc_6: 0.2333 - dense_3_acc_7: 0.3333 - dense_3_acc_8: 0.4167 - dense_3_acc_9: 0.3000 - dense_3_acc_10: 0.4667 - dense_3_acc_11: 0.3000 - dense_3_acc_12: 0.2667 - dense_3_acc_13: 0.3833 - dense_3_acc_14: 0.3833 - dense_3_acc_15: 0.4000 - dense_3_acc_16: 0.3000 - dense_3_acc_17: 0.3667 - dense_3_acc_18: 0.2333 - dense_3_acc_19: 0.3333 - dense_3_acc_20: 0.4000 - dense_3_acc_21: 0.3333 - dense_3_acc_22: 0.2833 - dense_3_acc_23: 0.3833 - dense_3_acc_24: 0.3000 - dense_3_acc_25: 0.2167 - dense_3_acc_26: 0.4667 - dense_3_acc_27: 0.3833 - dense_3_acc_28: 0.3167 - dense_3_acc_29: 0.2667 - dense_3_acc_30: 0.0000e+00     
Epoch 16/100
60/60 [==============================] - 0s - loss: 69.6348 - dense_3_loss_1: 4.1465 - dense_3_loss_2: 3.7322 - dense_3_loss_3: 3.2417 - dense_3_loss_4: 3.0877 - dense_3_loss_5: 2.7567 - dense_3_loss_6: 2.6226 - dense_3_loss_7: 2.5686 - dense_3_loss_8: 2.2400 - dense_3_loss_9: 2.3391 - dense_3_loss_10: 2.1062 - dense_3_loss_11: 2.3192 - dense_3_loss_12: 2.2351 - dense_3_loss_13: 2.1850 - dense_3_loss_14: 2.1130 - dense_3_loss_15: 2.1787 - dense_3_loss_16: 2.2488 - dense_3_loss_17: 2.0323 - dense_3_loss_18: 2.1965 - dense_3_loss_19: 2.0803 - dense_3_loss_20: 2.0801 - dense_3_loss_21: 2.0976 - dense_3_loss_22: 2.0621 - dense_3_loss_23: 2.1558 - dense_3_loss_24: 1.9809 - dense_3_loss_25: 2.3298 - dense_3_loss_26: 1.9380 - dense_3_loss_27: 2.2280 - dense_3_loss_28: 2.0997 - dense_3_loss_29: 2.2324 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.3000 - dense_3_acc_4: 0.2500 - dense_3_acc_5: 0.3667 - dense_3_acc_6: 0.3000 - dense_3_acc_7: 0.3667 - dense_3_acc_8: 0.3833 - dense_3_acc_9: 0.3667 - dense_3_acc_10: 0.5000 - dense_3_acc_11: 0.3167 - dense_3_acc_12: 0.3333 - dense_3_acc_13: 0.4000 - dense_3_acc_14: 0.3833 - dense_3_acc_15: 0.4667 - dense_3_acc_16: 0.3167 - dense_3_acc_17: 0.4167 - dense_3_acc_18: 0.2833 - dense_3_acc_19: 0.4167 - dense_3_acc_20: 0.4667 - dense_3_acc_21: 0.3667 - dense_3_acc_22: 0.2833 - dense_3_acc_23: 0.3833 - dense_3_acc_24: 0.4167 - dense_3_acc_25: 0.1667 - dense_3_acc_26: 0.5167 - dense_3_acc_27: 0.4167 - dense_3_acc_28: 0.3667 - dense_3_acc_29: 0.3500 - dense_3_acc_30: 0.0167         
Epoch 17/100
60/60 [==============================] - 0s - loss: 65.9901 - dense_3_loss_1: 4.1396 - dense_3_loss_2: 3.6883 - dense_3_loss_3: 3.1620 - dense_3_loss_4: 2.9752 - dense_3_loss_5: 2.6275 - dense_3_loss_6: 2.4767 - dense_3_loss_7: 2.4233 - dense_3_loss_8: 2.0940 - dense_3_loss_9: 2.1977 - dense_3_loss_10: 1.9932 - dense_3_loss_11: 2.1710 - dense_3_loss_12: 2.0613 - dense_3_loss_13: 2.0358 - dense_3_loss_14: 1.9602 - dense_3_loss_15: 2.0243 - dense_3_loss_16: 2.1427 - dense_3_loss_17: 1.9176 - dense_3_loss_18: 2.0482 - dense_3_loss_19: 1.9735 - dense_3_loss_20: 1.9584 - dense_3_loss_21: 1.9639 - dense_3_loss_22: 1.9326 - dense_3_loss_23: 2.0045 - dense_3_loss_24: 1.8529 - dense_3_loss_25: 2.1371 - dense_3_loss_26: 1.9201 - dense_3_loss_27: 2.1075 - dense_3_loss_28: 1.9920 - dense_3_loss_29: 2.0090 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.3167 - dense_3_acc_4: 0.2667 - dense_3_acc_5: 0.3833 - dense_3_acc_6: 0.3000 - dense_3_acc_7: 0.4000 - dense_3_acc_8: 0.3833 - dense_3_acc_9: 0.4000 - dense_3_acc_10: 0.5667 - dense_3_acc_11: 0.3167 - dense_3_acc_12: 0.4667 - dense_3_acc_13: 0.3667 - dense_3_acc_14: 0.4333 - dense_3_acc_15: 0.4833 - dense_3_acc_16: 0.3167 - dense_3_acc_17: 0.4500 - dense_3_acc_18: 0.3667 - dense_3_acc_19: 0.4167 - dense_3_acc_20: 0.5000 - dense_3_acc_21: 0.4500 - dense_3_acc_22: 0.4500 - dense_3_acc_23: 0.4500 - dense_3_acc_24: 0.4667 - dense_3_acc_25: 0.2500 - dense_3_acc_26: 0.4833 - dense_3_acc_27: 0.4833 - dense_3_acc_28: 0.4167 - dense_3_acc_29: 0.4500 - dense_3_acc_30: 0.0333     
Epoch 18/100
60/60 [==============================] - 0s - loss: 62.4254 - dense_3_loss_1: 4.1316 - dense_3_loss_2: 3.6412 - dense_3_loss_3: 3.0758 - dense_3_loss_4: 2.8576 - dense_3_loss_5: 2.4986 - dense_3_loss_6: 2.3372 - dense_3_loss_7: 2.3021 - dense_3_loss_8: 1.9771 - dense_3_loss_9: 2.0699 - dense_3_loss_10: 1.8763 - dense_3_loss_11: 1.9800 - dense_3_loss_12: 1.9162 - dense_3_loss_13: 1.8812 - dense_3_loss_14: 1.8353 - dense_3_loss_15: 1.8937 - dense_3_loss_16: 1.9882 - dense_3_loss_17: 1.8149 - dense_3_loss_18: 1.9091 - dense_3_loss_19: 1.7925 - dense_3_loss_20: 1.8301 - dense_3_loss_21: 1.8566 - dense_3_loss_22: 1.8305 - dense_3_loss_23: 1.8466 - dense_3_loss_24: 1.7697 - dense_3_loss_25: 2.0074 - dense_3_loss_26: 1.8083 - dense_3_loss_27: 1.9995 - dense_3_loss_28: 1.8476 - dense_3_loss_29: 1.8506 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3333 - dense_3_acc_4: 0.2833 - dense_3_acc_5: 0.3833 - dense_3_acc_6: 0.3000 - dense_3_acc_7: 0.4000 - dense_3_acc_8: 0.4667 - dense_3_acc_9: 0.4000 - dense_3_acc_10: 0.5333 - dense_3_acc_11: 0.4167 - dense_3_acc_12: 0.5333 - dense_3_acc_13: 0.5167 - dense_3_acc_14: 0.4333 - dense_3_acc_15: 0.5167 - dense_3_acc_16: 0.3833 - dense_3_acc_17: 0.5000 - dense_3_acc_18: 0.4167 - dense_3_acc_19: 0.5500 - dense_3_acc_20: 0.5833 - dense_3_acc_21: 0.4500 - dense_3_acc_22: 0.5167 - dense_3_acc_23: 0.5000 - dense_3_acc_24: 0.4167 - dense_3_acc_25: 0.3500 - dense_3_acc_26: 0.4833 - dense_3_acc_27: 0.5167 - dense_3_acc_28: 0.4667 - dense_3_acc_29: 0.5167 - dense_3_acc_30: 0.0667     
Epoch 19/100
60/60 [==============================] - 0s - loss: 59.1936 - dense_3_loss_1: 4.1227 - dense_3_loss_2: 3.5951 - dense_3_loss_3: 2.9869 - dense_3_loss_4: 2.7447 - dense_3_loss_5: 2.3741 - dense_3_loss_6: 2.2121 - dense_3_loss_7: 2.1506 - dense_3_loss_8: 1.8410 - dense_3_loss_9: 1.9615 - dense_3_loss_10: 1.7706 - dense_3_loss_11: 1.8378 - dense_3_loss_12: 1.8086 - dense_3_loss_13: 1.7380 - dense_3_loss_14: 1.7354 - dense_3_loss_15: 1.7593 - dense_3_loss_16: 1.8701 - dense_3_loss_17: 1.6994 - dense_3_loss_18: 1.7931 - dense_3_loss_19: 1.7014 - dense_3_loss_20: 1.7203 - dense_3_loss_21: 1.7382 - dense_3_loss_22: 1.7411 - dense_3_loss_23: 1.7450 - dense_3_loss_24: 1.6392 - dense_3_loss_25: 1.8526 - dense_3_loss_26: 1.7408 - dense_3_loss_27: 1.8632 - dense_3_loss_28: 1.7257 - dense_3_loss_29: 1.7250 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3333 - dense_3_acc_4: 0.3167 - dense_3_acc_5: 0.4000 - dense_3_acc_6: 0.3500 - dense_3_acc_7: 0.3833 - dense_3_acc_8: 0.4667 - dense_3_acc_9: 0.4333 - dense_3_acc_10: 0.5667 - dense_3_acc_11: 0.4333 - dense_3_acc_12: 0.5333 - dense_3_acc_13: 0.5167 - dense_3_acc_14: 0.4500 - dense_3_acc_15: 0.5500 - dense_3_acc_16: 0.4167 - dense_3_acc_17: 0.5167 - dense_3_acc_18: 0.5167 - dense_3_acc_19: 0.5333 - dense_3_acc_20: 0.6000 - dense_3_acc_21: 0.5667 - dense_3_acc_22: 0.5833 - dense_3_acc_23: 0.6167 - dense_3_acc_24: 0.6000 - dense_3_acc_25: 0.4667 - dense_3_acc_26: 0.5333 - dense_3_acc_27: 0.5167 - dense_3_acc_28: 0.5167 - dense_3_acc_29: 0.5667 - dense_3_acc_30: 0.0667     
Epoch 20/100
60/60 [==============================] - 0s - loss: 55.9041 - dense_3_loss_1: 4.1136 - dense_3_loss_2: 3.5456 - dense_3_loss_3: 2.8907 - dense_3_loss_4: 2.6332 - dense_3_loss_5: 2.2483 - dense_3_loss_6: 2.0743 - dense_3_loss_7: 1.9941 - dense_3_loss_8: 1.7040 - dense_3_loss_9: 1.8334 - dense_3_loss_10: 1.6292 - dense_3_loss_11: 1.6910 - dense_3_loss_12: 1.6987 - dense_3_loss_13: 1.6013 - dense_3_loss_14: 1.5575 - dense_3_loss_15: 1.6839 - dense_3_loss_16: 1.7210 - dense_3_loss_17: 1.5920 - dense_3_loss_18: 1.6572 - dense_3_loss_19: 1.5849 - dense_3_loss_20: 1.6037 - dense_3_loss_21: 1.6281 - dense_3_loss_22: 1.6090 - dense_3_loss_23: 1.6191 - dense_3_loss_24: 1.5684 - dense_3_loss_25: 1.7749 - dense_3_loss_26: 1.6105 - dense_3_loss_27: 1.7867 - dense_3_loss_28: 1.6119 - dense_3_loss_29: 1.6378 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3333 - dense_3_acc_4: 0.3000 - dense_3_acc_5: 0.4000 - dense_3_acc_6: 0.3833 - dense_3_acc_7: 0.4500 - dense_3_acc_8: 0.5000 - dense_3_acc_9: 0.4833 - dense_3_acc_10: 0.5667 - dense_3_acc_11: 0.4833 - dense_3_acc_12: 0.5000 - dense_3_acc_13: 0.6167 - dense_3_acc_14: 0.5167 - dense_3_acc_15: 0.5167 - dense_3_acc_16: 0.5167 - dense_3_acc_17: 0.5333 - dense_3_acc_18: 0.4500 - dense_3_acc_19: 0.5333 - dense_3_acc_20: 0.5833 - dense_3_acc_21: 0.5500 - dense_3_acc_22: 0.5500 - dense_3_acc_23: 0.6000 - dense_3_acc_24: 0.5500 - dense_3_acc_25: 0.4500 - dense_3_acc_26: 0.5833 - dense_3_acc_27: 0.5333 - dense_3_acc_28: 0.5333 - dense_3_acc_29: 0.6167 - dense_3_acc_30: 0.0833     
Epoch 21/100
60/60 [==============================] - 0s - loss: 53.0456 - dense_3_loss_1: 4.1057 - dense_3_loss_2: 3.4933 - dense_3_loss_3: 2.7971 - dense_3_loss_4: 2.5257 - dense_3_loss_5: 2.1240 - dense_3_loss_6: 1.9231 - dense_3_loss_7: 1.8383 - dense_3_loss_8: 1.6111 - dense_3_loss_9: 1.7245 - dense_3_loss_10: 1.5677 - dense_3_loss_11: 1.5712 - dense_3_loss_12: 1.5929 - dense_3_loss_13: 1.5167 - dense_3_loss_14: 1.4751 - dense_3_loss_15: 1.5895 - dense_3_loss_16: 1.6165 - dense_3_loss_17: 1.4809 - dense_3_loss_18: 1.5853 - dense_3_loss_19: 1.5189 - dense_3_loss_20: 1.5085 - dense_3_loss_21: 1.5121 - dense_3_loss_22: 1.4997 - dense_3_loss_23: 1.5729 - dense_3_loss_24: 1.4955 - dense_3_loss_25: 1.5616 - dense_3_loss_26: 1.5122 - dense_3_loss_27: 1.6396 - dense_3_loss_28: 1.4848 - dense_3_loss_29: 1.6015 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3333 - dense_3_acc_4: 0.3333 - dense_3_acc_5: 0.3833 - dense_3_acc_6: 0.4167 - dense_3_acc_7: 0.4333 - dense_3_acc_8: 0.5333 - dense_3_acc_9: 0.5500 - dense_3_acc_10: 0.5833 - dense_3_acc_11: 0.5500 - dense_3_acc_12: 0.5333 - dense_3_acc_13: 0.6833 - dense_3_acc_14: 0.6333 - dense_3_acc_15: 0.4667 - dense_3_acc_16: 0.6000 - dense_3_acc_17: 0.6833 - dense_3_acc_18: 0.5667 - dense_3_acc_19: 0.6667 - dense_3_acc_20: 0.6667 - dense_3_acc_21: 0.5833 - dense_3_acc_22: 0.6500 - dense_3_acc_23: 0.6000 - dense_3_acc_24: 0.6167 - dense_3_acc_25: 0.5833 - dense_3_acc_26: 0.6833 - dense_3_acc_27: 0.5667 - dense_3_acc_28: 0.7167 - dense_3_acc_29: 0.6833 - dense_3_acc_30: 0.0833     
Epoch 22/100
60/60 [==============================] - 0s - loss: 50.3733 - dense_3_loss_1: 4.0969 - dense_3_loss_2: 3.4404 - dense_3_loss_3: 2.7042 - dense_3_loss_4: 2.4104 - dense_3_loss_5: 2.0199 - dense_3_loss_6: 1.7992 - dense_3_loss_7: 1.7074 - dense_3_loss_8: 1.4952 - dense_3_loss_9: 1.5910 - dense_3_loss_10: 1.4579 - dense_3_loss_11: 1.4937 - dense_3_loss_12: 1.4928 - dense_3_loss_13: 1.3477 - dense_3_loss_14: 1.4210 - dense_3_loss_15: 1.4520 - dense_3_loss_16: 1.5343 - dense_3_loss_17: 1.3811 - dense_3_loss_18: 1.4633 - dense_3_loss_19: 1.3704 - dense_3_loss_20: 1.3674 - dense_3_loss_21: 1.4583 - dense_3_loss_22: 1.4996 - dense_3_loss_23: 1.5074 - dense_3_loss_24: 1.4457 - dense_3_loss_25: 1.4325 - dense_3_loss_26: 1.4355 - dense_3_loss_27: 1.5845 - dense_3_loss_28: 1.4376 - dense_3_loss_29: 1.5259 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3500 - dense_3_acc_4: 0.3167 - dense_3_acc_5: 0.4000 - dense_3_acc_6: 0.4500 - dense_3_acc_7: 0.4833 - dense_3_acc_8: 0.5500 - dense_3_acc_9: 0.5500 - dense_3_acc_10: 0.5833 - dense_3_acc_11: 0.6333 - dense_3_acc_12: 0.5333 - dense_3_acc_13: 0.7667 - dense_3_acc_14: 0.6167 - dense_3_acc_15: 0.6000 - dense_3_acc_16: 0.5833 - dense_3_acc_17: 0.6167 - dense_3_acc_18: 0.5833 - dense_3_acc_19: 0.6500 - dense_3_acc_20: 0.7167 - dense_3_acc_21: 0.6000 - dense_3_acc_22: 0.6000 - dense_3_acc_23: 0.6167 - dense_3_acc_24: 0.5833 - dense_3_acc_25: 0.6167 - dense_3_acc_26: 0.6167 - dense_3_acc_27: 0.5500 - dense_3_acc_28: 0.7000 - dense_3_acc_29: 0.6833 - dense_3_acc_30: 0.1000     
Epoch 23/100
60/60 [==============================] - 0s - loss: 47.1136 - dense_3_loss_1: 4.0877 - dense_3_loss_2: 3.3881 - dense_3_loss_3: 2.6065 - dense_3_loss_4: 2.2940 - dense_3_loss_5: 1.9145 - dense_3_loss_6: 1.6671 - dense_3_loss_7: 1.5894 - dense_3_loss_8: 1.4163 - dense_3_loss_9: 1.5006 - dense_3_loss_10: 1.3474 - dense_3_loss_11: 1.3440 - dense_3_loss_12: 1.3328 - dense_3_loss_13: 1.2923 - dense_3_loss_14: 1.2724 - dense_3_loss_15: 1.3735 - dense_3_loss_16: 1.3699 - dense_3_loss_17: 1.3033 - dense_3_loss_18: 1.3340 - dense_3_loss_19: 1.2821 - dense_3_loss_20: 1.3115 - dense_3_loss_21: 1.3068 - dense_3_loss_22: 1.3032 - dense_3_loss_23: 1.3661 - dense_3_loss_24: 1.3052 - dense_3_loss_25: 1.3722 - dense_3_loss_26: 1.3014 - dense_3_loss_27: 1.4181 - dense_3_loss_28: 1.3068 - dense_3_loss_29: 1.4068 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3000 - dense_3_acc_3: 0.3667 - dense_3_acc_4: 0.3167 - dense_3_acc_5: 0.4500 - dense_3_acc_6: 0.4833 - dense_3_acc_7: 0.5833 - dense_3_acc_8: 0.6000 - dense_3_acc_9: 0.6167 - dense_3_acc_10: 0.6500 - dense_3_acc_11: 0.6833 - dense_3_acc_12: 0.8000 - dense_3_acc_13: 0.8000 - dense_3_acc_14: 0.8000 - dense_3_acc_15: 0.6000 - dense_3_acc_16: 0.6500 - dense_3_acc_17: 0.7000 - dense_3_acc_18: 0.7000 - dense_3_acc_19: 0.7333 - dense_3_acc_20: 0.7833 - dense_3_acc_21: 0.7000 - dense_3_acc_22: 0.7333 - dense_3_acc_23: 0.6667 - dense_3_acc_24: 0.7500 - dense_3_acc_25: 0.6167 - dense_3_acc_26: 0.6500 - dense_3_acc_27: 0.6667 - dense_3_acc_28: 0.7000 - dense_3_acc_29: 0.7167 - dense_3_acc_30: 0.0667     
Epoch 24/100
60/60 [==============================] - 0s - loss: 44.5415 - dense_3_loss_1: 4.0795 - dense_3_loss_2: 3.3374 - dense_3_loss_3: 2.5130 - dense_3_loss_4: 2.1804 - dense_3_loss_5: 1.8117 - dense_3_loss_6: 1.5359 - dense_3_loss_7: 1.4634 - dense_3_loss_8: 1.2881 - dense_3_loss_9: 1.4308 - dense_3_loss_10: 1.2597 - dense_3_loss_11: 1.3093 - dense_3_loss_12: 1.2079 - dense_3_loss_13: 1.1761 - dense_3_loss_14: 1.2177 - dense_3_loss_15: 1.2920 - dense_3_loss_16: 1.2170 - dense_3_loss_17: 1.2366 - dense_3_loss_18: 1.2358 - dense_3_loss_19: 1.1933 - dense_3_loss_20: 1.2482 - dense_3_loss_21: 1.2445 - dense_3_loss_22: 1.1931 - dense_3_loss_23: 1.2510 - dense_3_loss_24: 1.2243 - dense_3_loss_25: 1.3455 - dense_3_loss_26: 1.1807 - dense_3_loss_27: 1.3201 - dense_3_loss_28: 1.2483 - dense_3_loss_29: 1.3001 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3167 - dense_3_acc_3: 0.4167 - dense_3_acc_4: 0.3500 - dense_3_acc_5: 0.4333 - dense_3_acc_6: 0.6333 - dense_3_acc_7: 0.7500 - dense_3_acc_8: 0.6833 - dense_3_acc_9: 0.5667 - dense_3_acc_10: 0.7000 - dense_3_acc_11: 0.6500 - dense_3_acc_12: 0.8167 - dense_3_acc_13: 0.8333 - dense_3_acc_14: 0.7667 - dense_3_acc_15: 0.6333 - dense_3_acc_16: 0.7167 - dense_3_acc_17: 0.7500 - dense_3_acc_18: 0.7333 - dense_3_acc_19: 0.8167 - dense_3_acc_20: 0.7833 - dense_3_acc_21: 0.7333 - dense_3_acc_22: 0.7667 - dense_3_acc_23: 0.7500 - dense_3_acc_24: 0.8333 - dense_3_acc_25: 0.6500 - dense_3_acc_26: 0.7333 - dense_3_acc_27: 0.6667 - dense_3_acc_28: 0.7667 - dense_3_acc_29: 0.7500 - dense_3_acc_30: 0.0833         
Epoch 25/100
60/60 [==============================] - 0s - loss: 41.9117 - dense_3_loss_1: 4.0712 - dense_3_loss_2: 3.2843 - dense_3_loss_3: 2.4203 - dense_3_loss_4: 2.0731 - dense_3_loss_5: 1.7246 - dense_3_loss_6: 1.4250 - dense_3_loss_7: 1.3365 - dense_3_loss_8: 1.2414 - dense_3_loss_9: 1.2972 - dense_3_loss_10: 1.1589 - dense_3_loss_11: 1.1577 - dense_3_loss_12: 1.1190 - dense_3_loss_13: 1.0514 - dense_3_loss_14: 1.1068 - dense_3_loss_15: 1.1633 - dense_3_loss_16: 1.1493 - dense_3_loss_17: 1.1351 - dense_3_loss_18: 1.1266 - dense_3_loss_19: 1.1306 - dense_3_loss_20: 1.1544 - dense_3_loss_21: 1.1338 - dense_3_loss_22: 1.1670 - dense_3_loss_23: 1.2158 - dense_3_loss_24: 1.1016 - dense_3_loss_25: 1.2215 - dense_3_loss_26: 1.1441 - dense_3_loss_27: 1.2259 - dense_3_loss_28: 1.1517 - dense_3_loss_29: 1.2236 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3167 - dense_3_acc_3: 0.4333 - dense_3_acc_4: 0.4000 - dense_3_acc_5: 0.4667 - dense_3_acc_6: 0.6667 - dense_3_acc_7: 0.8000 - dense_3_acc_8: 0.6667 - dense_3_acc_9: 0.7500 - dense_3_acc_10: 0.7333 - dense_3_acc_11: 0.8167 - dense_3_acc_12: 0.8333 - dense_3_acc_13: 0.8667 - dense_3_acc_14: 0.8333 - dense_3_acc_15: 0.8167 - dense_3_acc_16: 0.7833 - dense_3_acc_17: 0.8500 - dense_3_acc_18: 0.8167 - dense_3_acc_19: 0.8000 - dense_3_acc_20: 0.8500 - dense_3_acc_21: 0.8333 - dense_3_acc_22: 0.7667 - dense_3_acc_23: 0.6667 - dense_3_acc_24: 0.8167 - dense_3_acc_25: 0.7000 - dense_3_acc_26: 0.7333 - dense_3_acc_27: 0.7000 - dense_3_acc_28: 0.8000 - dense_3_acc_29: 0.7333 - dense_3_acc_30: 0.1000         
Epoch 26/100
60/60 [==============================] - 0s - loss: 39.3949 - dense_3_loss_1: 4.0639 - dense_3_loss_2: 3.2298 - dense_3_loss_3: 2.3242 - dense_3_loss_4: 1.9697 - dense_3_loss_5: 1.6139 - dense_3_loss_6: 1.3173 - dense_3_loss_7: 1.2134 - dense_3_loss_8: 1.1094 - dense_3_loss_9: 1.2541 - dense_3_loss_10: 1.0632 - dense_3_loss_11: 1.0924 - dense_3_loss_12: 1.0295 - dense_3_loss_13: 1.0115 - dense_3_loss_14: 1.0101 - dense_3_loss_15: 1.0586 - dense_3_loss_16: 1.0785 - dense_3_loss_17: 1.0281 - dense_3_loss_18: 1.0352 - dense_3_loss_19: 1.0295 - dense_3_loss_20: 1.1022 - dense_3_loss_21: 1.0659 - dense_3_loss_22: 1.0719 - dense_3_loss_23: 1.1374 - dense_3_loss_24: 0.9864 - dense_3_loss_25: 1.1276 - dense_3_loss_26: 1.0498 - dense_3_loss_27: 1.1201 - dense_3_loss_28: 1.0603 - dense_3_loss_29: 1.1412 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.3167 - dense_3_acc_3: 0.4833 - dense_3_acc_4: 0.4500 - dense_3_acc_5: 0.5333 - dense_3_acc_6: 0.7500 - dense_3_acc_7: 0.8500 - dense_3_acc_8: 0.8000 - dense_3_acc_9: 0.6667 - dense_3_acc_10: 0.7833 - dense_3_acc_11: 0.8167 - dense_3_acc_12: 0.8000 - dense_3_acc_13: 0.9000 - dense_3_acc_14: 0.8833 - dense_3_acc_15: 0.8333 - dense_3_acc_16: 0.8833 - dense_3_acc_17: 0.8833 - dense_3_acc_18: 0.9000 - dense_3_acc_19: 0.9000 - dense_3_acc_20: 0.8667 - dense_3_acc_21: 0.8167 - dense_3_acc_22: 0.8000 - dense_3_acc_23: 0.7500 - dense_3_acc_24: 0.9167 - dense_3_acc_25: 0.7000 - dense_3_acc_26: 0.8167 - dense_3_acc_27: 0.7167 - dense_3_acc_28: 0.8500 - dense_3_acc_29: 0.7667 - dense_3_acc_30: 0.0833     
Epoch 27/100
60/60 [==============================] - 0s - loss: 36.9648 - dense_3_loss_1: 4.0547 - dense_3_loss_2: 3.1770 - dense_3_loss_3: 2.2368 - dense_3_loss_4: 1.8821 - dense_3_loss_5: 1.5305 - dense_3_loss_6: 1.2287 - dense_3_loss_7: 1.1056 - dense_3_loss_8: 1.0265 - dense_3_loss_9: 1.1430 - dense_3_loss_10: 0.9467 - dense_3_loss_11: 0.9973 - dense_3_loss_12: 0.9338 - dense_3_loss_13: 0.8867 - dense_3_loss_14: 0.9179 - dense_3_loss_15: 0.9619 - dense_3_loss_16: 0.9662 - dense_3_loss_17: 0.9239 - dense_3_loss_18: 0.9593 - dense_3_loss_19: 0.9909 - dense_3_loss_20: 0.9883 - dense_3_loss_21: 0.9969 - dense_3_loss_22: 0.9712 - dense_3_loss_23: 1.0654 - dense_3_loss_24: 0.9362 - dense_3_loss_25: 1.0748 - dense_3_loss_26: 0.9513 - dense_3_loss_27: 1.0280 - dense_3_loss_28: 1.0056 - dense_3_loss_29: 1.0777 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0833 - dense_3_acc_2: 0.2833 - dense_3_acc_3: 0.5167 - dense_3_acc_4: 0.4500 - dense_3_acc_5: 0.5500 - dense_3_acc_6: 0.7833 - dense_3_acc_7: 0.8833 - dense_3_acc_8: 0.8167 - dense_3_acc_9: 0.7833 - dense_3_acc_10: 0.9167 - dense_3_acc_11: 0.8833 - dense_3_acc_12: 0.9333 - dense_3_acc_13: 0.9333 - dense_3_acc_14: 0.9167 - dense_3_acc_15: 0.8833 - dense_3_acc_16: 0.8667 - dense_3_acc_17: 0.8500 - dense_3_acc_18: 0.8833 - dense_3_acc_19: 0.8667 - dense_3_acc_20: 0.8833 - dense_3_acc_21: 0.8833 - dense_3_acc_22: 0.8667 - dense_3_acc_23: 0.7333 - dense_3_acc_24: 0.8667 - dense_3_acc_25: 0.7833 - dense_3_acc_26: 0.8500 - dense_3_acc_27: 0.8333 - dense_3_acc_28: 0.8667 - dense_3_acc_29: 0.7667 - dense_3_acc_30: 0.1000     
Epoch 28/100
60/60 [==============================] - 0s - loss: 34.8167 - dense_3_loss_1: 4.0461 - dense_3_loss_2: 3.1214 - dense_3_loss_3: 2.1449 - dense_3_loss_4: 1.8042 - dense_3_loss_5: 1.4641 - dense_3_loss_6: 1.1444 - dense_3_loss_7: 1.0001 - dense_3_loss_8: 0.9576 - dense_3_loss_9: 1.0352 - dense_3_loss_10: 0.8585 - dense_3_loss_11: 0.8932 - dense_3_loss_12: 0.9183 - dense_3_loss_13: 0.7938 - dense_3_loss_14: 0.8294 - dense_3_loss_15: 0.8961 - dense_3_loss_16: 0.9200 - dense_3_loss_17: 0.8627 - dense_3_loss_18: 0.8716 - dense_3_loss_19: 0.9265 - dense_3_loss_20: 0.9207 - dense_3_loss_21: 0.9111 - dense_3_loss_22: 0.8836 - dense_3_loss_23: 0.9663 - dense_3_loss_24: 0.8877 - dense_3_loss_25: 1.0000 - dense_3_loss_26: 0.8753 - dense_3_loss_27: 0.9828 - dense_3_loss_28: 0.9140 - dense_3_loss_29: 0.9870 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3333 - dense_3_acc_3: 0.5333 - dense_3_acc_4: 0.4500 - dense_3_acc_5: 0.5500 - dense_3_acc_6: 0.7833 - dense_3_acc_7: 0.8833 - dense_3_acc_8: 0.7833 - dense_3_acc_9: 0.8167 - dense_3_acc_10: 0.9500 - dense_3_acc_11: 0.9167 - dense_3_acc_12: 0.9167 - dense_3_acc_13: 0.9167 - dense_3_acc_14: 0.9333 - dense_3_acc_15: 0.9333 - dense_3_acc_16: 0.8833 - dense_3_acc_17: 0.9000 - dense_3_acc_18: 0.9333 - dense_3_acc_19: 0.9000 - dense_3_acc_20: 0.9000 - dense_3_acc_21: 0.9333 - dense_3_acc_22: 0.9000 - dense_3_acc_23: 0.8667 - dense_3_acc_24: 0.8833 - dense_3_acc_25: 0.8167 - dense_3_acc_26: 0.9000 - dense_3_acc_27: 0.8667 - dense_3_acc_28: 0.9000 - dense_3_acc_29: 0.8500 - dense_3_acc_30: 0.0833     
Epoch 29/100
60/60 [==============================] - 0s - loss: 32.7231 - dense_3_loss_1: 4.0386 - dense_3_loss_2: 3.0681 - dense_3_loss_3: 2.0648 - dense_3_loss_4: 1.7128 - dense_3_loss_5: 1.3638 - dense_3_loss_6: 1.0524 - dense_3_loss_7: 0.9198 - dense_3_loss_8: 0.8479 - dense_3_loss_9: 0.9925 - dense_3_loss_10: 0.7841 - dense_3_loss_11: 0.8447 - dense_3_loss_12: 0.8156 - dense_3_loss_13: 0.7348 - dense_3_loss_14: 0.7806 - dense_3_loss_15: 0.8010 - dense_3_loss_16: 0.8083 - dense_3_loss_17: 0.7914 - dense_3_loss_18: 0.8029 - dense_3_loss_19: 0.8613 - dense_3_loss_20: 0.8451 - dense_3_loss_21: 0.8712 - dense_3_loss_22: 0.8310 - dense_3_loss_23: 0.8943 - dense_3_loss_24: 0.8076 - dense_3_loss_25: 0.8985 - dense_3_loss_26: 0.8124 - dense_3_loss_27: 0.8882 - dense_3_loss_28: 0.8758 - dense_3_loss_29: 0.9136 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3333 - dense_3_acc_3: 0.5667 - dense_3_acc_4: 0.4833 - dense_3_acc_5: 0.6667 - dense_3_acc_6: 0.8333 - dense_3_acc_7: 0.9333 - dense_3_acc_8: 0.9167 - dense_3_acc_9: 0.8667 - dense_3_acc_10: 0.9167 - dense_3_acc_11: 0.9500 - dense_3_acc_12: 0.9000 - dense_3_acc_13: 0.9500 - dense_3_acc_14: 0.9333 - dense_3_acc_15: 0.9500 - dense_3_acc_16: 0.9333 - dense_3_acc_17: 0.9333 - dense_3_acc_18: 0.9833 - dense_3_acc_19: 0.9167 - dense_3_acc_20: 0.9333 - dense_3_acc_21: 0.8667 - dense_3_acc_22: 0.8833 - dense_3_acc_23: 0.9333 - dense_3_acc_24: 0.9500 - dense_3_acc_25: 0.8333 - dense_3_acc_26: 0.9333 - dense_3_acc_27: 0.8833 - dense_3_acc_28: 0.9167 - dense_3_acc_29: 0.8333 - dense_3_acc_30: 0.0833     
Epoch 30/100
60/60 [==============================] - 0s - loss: 30.7184 - dense_3_loss_1: 4.0317 - dense_3_loss_2: 3.0110 - dense_3_loss_3: 1.9882 - dense_3_loss_4: 1.6103 - dense_3_loss_5: 1.2771 - dense_3_loss_6: 0.9582 - dense_3_loss_7: 0.8454 - dense_3_loss_8: 0.7935 - dense_3_loss_9: 0.8754 - dense_3_loss_10: 0.7102 - dense_3_loss_11: 0.7615 - dense_3_loss_12: 0.7282 - dense_3_loss_13: 0.6609 - dense_3_loss_14: 0.7135 - dense_3_loss_15: 0.7330 - dense_3_loss_16: 0.7330 - dense_3_loss_17: 0.7167 - dense_3_loss_18: 0.7357 - dense_3_loss_19: 0.7850 - dense_3_loss_20: 0.7898 - dense_3_loss_21: 0.8095 - dense_3_loss_22: 0.7739 - dense_3_loss_23: 0.8293 - dense_3_loss_24: 0.7147 - dense_3_loss_25: 0.8563 - dense_3_loss_26: 0.7548 - dense_3_loss_27: 0.8392 - dense_3_loss_28: 0.8232 - dense_3_loss_29: 0.8589 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.3667 - dense_3_acc_3: 0.5833 - dense_3_acc_4: 0.5167 - dense_3_acc_5: 0.6833 - dense_3_acc_6: 0.8500 - dense_3_acc_7: 0.9500 - dense_3_acc_8: 0.9333 - dense_3_acc_9: 0.8833 - dense_3_acc_10: 0.9667 - dense_3_acc_11: 0.9500 - dense_3_acc_12: 0.9500 - dense_3_acc_13: 0.9833 - dense_3_acc_14: 0.9667 - dense_3_acc_15: 0.9500 - dense_3_acc_16: 0.9667 - dense_3_acc_17: 0.9667 - dense_3_acc_18: 0.9833 - dense_3_acc_19: 0.9333 - dense_3_acc_20: 0.9333 - dense_3_acc_21: 0.9500 - dense_3_acc_22: 0.9167 - dense_3_acc_23: 0.9167 - dense_3_acc_24: 0.9833 - dense_3_acc_25: 0.8667 - dense_3_acc_26: 0.9333 - dense_3_acc_27: 0.9000 - dense_3_acc_28: 0.9167 - dense_3_acc_29: 0.8833 - dense_3_acc_30: 0.0833     
Epoch 31/100
60/60 [==============================] - 0s - loss: 28.8140 - dense_3_loss_1: 4.0242 - dense_3_loss_2: 2.9587 - dense_3_loss_3: 1.9138 - dense_3_loss_4: 1.5242 - dense_3_loss_5: 1.2031 - dense_3_loss_6: 0.8865 - dense_3_loss_7: 0.7862 - dense_3_loss_8: 0.7396 - dense_3_loss_9: 0.7913 - dense_3_loss_10: 0.6598 - dense_3_loss_11: 0.6848 - dense_3_loss_12: 0.6821 - dense_3_loss_13: 0.6007 - dense_3_loss_14: 0.6477 - dense_3_loss_15: 0.6726 - dense_3_loss_16: 0.6804 - dense_3_loss_17: 0.6538 - dense_3_loss_18: 0.6776 - dense_3_loss_19: 0.7147 - dense_3_loss_20: 0.7335 - dense_3_loss_21: 0.7405 - dense_3_loss_22: 0.7116 - dense_3_loss_23: 0.7465 - dense_3_loss_24: 0.6515 - dense_3_loss_25: 0.7824 - dense_3_loss_26: 0.6800 - dense_3_loss_27: 0.7467 - dense_3_loss_28: 0.7477 - dense_3_loss_29: 0.7718 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4000 - dense_3_acc_3: 0.5833 - dense_3_acc_4: 0.5167 - dense_3_acc_5: 0.7167 - dense_3_acc_6: 0.9000 - dense_3_acc_7: 0.9500 - dense_3_acc_8: 0.9333 - dense_3_acc_9: 0.9000 - dense_3_acc_10: 0.9667 - dense_3_acc_11: 0.9667 - dense_3_acc_12: 0.9667 - dense_3_acc_13: 0.9833 - dense_3_acc_14: 0.9500 - dense_3_acc_15: 0.9500 - dense_3_acc_16: 0.9833 - dense_3_acc_17: 0.9667 - dense_3_acc_18: 0.9833 - dense_3_acc_19: 0.9500 - dense_3_acc_20: 0.9333 - dense_3_acc_21: 0.9333 - dense_3_acc_22: 0.9333 - dense_3_acc_23: 0.9500 - dense_3_acc_24: 0.9833 - dense_3_acc_25: 0.8500 - dense_3_acc_26: 0.9333 - dense_3_acc_27: 0.8833 - dense_3_acc_28: 0.9333 - dense_3_acc_29: 0.8833 - dense_3_acc_30: 0.0833     
Epoch 32/100
60/60 [==============================] - 0s - loss: 27.1040 - dense_3_loss_1: 4.0175 - dense_3_loss_2: 2.9052 - dense_3_loss_3: 1.8390 - dense_3_loss_4: 1.4348 - dense_3_loss_5: 1.1297 - dense_3_loss_6: 0.8197 - dense_3_loss_7: 0.7376 - dense_3_loss_8: 0.6615 - dense_3_loss_9: 0.7362 - dense_3_loss_10: 0.5961 - dense_3_loss_11: 0.6245 - dense_3_loss_12: 0.6265 - dense_3_loss_13: 0.5431 - dense_3_loss_14: 0.5861 - dense_3_loss_15: 0.6176 - dense_3_loss_16: 0.6081 - dense_3_loss_17: 0.5994 - dense_3_loss_18: 0.6203 - dense_3_loss_19: 0.6433 - dense_3_loss_20: 0.6768 - dense_3_loss_21: 0.6956 - dense_3_loss_22: 0.6681 - dense_3_loss_23: 0.6924 - dense_3_loss_24: 0.6065 - dense_3_loss_25: 0.7130 - dense_3_loss_26: 0.6332 - dense_3_loss_27: 0.6799 - dense_3_loss_28: 0.6762 - dense_3_loss_29: 0.7163 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4000 - dense_3_acc_3: 0.6000 - dense_3_acc_4: 0.5833 - dense_3_acc_5: 0.7333 - dense_3_acc_6: 0.9000 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9667 - dense_3_acc_9: 0.9333 - dense_3_acc_10: 0.9833 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 0.9667 - dense_3_acc_13: 0.9833 - dense_3_acc_14: 0.9667 - dense_3_acc_15: 0.9667 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 0.9667 - dense_3_acc_18: 0.9833 - dense_3_acc_19: 0.9500 - dense_3_acc_20: 0.9333 - dense_3_acc_21: 0.9333 - dense_3_acc_22: 0.9333 - dense_3_acc_23: 0.9500 - dense_3_acc_24: 0.9500 - dense_3_acc_25: 0.8833 - dense_3_acc_26: 0.9667 - dense_3_acc_27: 0.9000 - dense_3_acc_28: 0.9167 - dense_3_acc_29: 0.8667 - dense_3_acc_30: 0.0833     
Epoch 33/100
60/60 [==============================] - 0s - loss: 25.4826 - dense_3_loss_1: 4.0109 - dense_3_loss_2: 2.8505 - dense_3_loss_3: 1.7608 - dense_3_loss_4: 1.3470 - dense_3_loss_5: 1.0588 - dense_3_loss_6: 0.7559 - dense_3_loss_7: 0.6860 - dense_3_loss_8: 0.6075 - dense_3_loss_9: 0.6755 - dense_3_loss_10: 0.5301 - dense_3_loss_11: 0.5767 - dense_3_loss_12: 0.5745 - dense_3_loss_13: 0.4933 - dense_3_loss_14: 0.5334 - dense_3_loss_15: 0.5575 - dense_3_loss_16: 0.5380 - dense_3_loss_17: 0.5463 - dense_3_loss_18: 0.5635 - dense_3_loss_19: 0.5981 - dense_3_loss_20: 0.6174 - dense_3_loss_21: 0.6412 - dense_3_loss_22: 0.6024 - dense_3_loss_23: 0.6503 - dense_3_loss_24: 0.5570 - dense_3_loss_25: 0.6607 - dense_3_loss_26: 0.5839 - dense_3_loss_27: 0.6249 - dense_3_loss_28: 0.6211 - dense_3_loss_29: 0.6594 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4000 - dense_3_acc_3: 0.6333 - dense_3_acc_4: 0.6000 - dense_3_acc_5: 0.8500 - dense_3_acc_6: 0.9333 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 0.9833 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 0.9833 - dense_3_acc_15: 0.9667 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 0.9833 - dense_3_acc_18: 0.9833 - dense_3_acc_19: 0.9500 - dense_3_acc_20: 0.9667 - dense_3_acc_21: 0.9333 - dense_3_acc_22: 0.9500 - dense_3_acc_23: 0.9500 - dense_3_acc_24: 0.9500 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9667 - dense_3_acc_27: 0.9667 - dense_3_acc_28: 0.9333 - dense_3_acc_29: 0.9000 - dense_3_acc_30: 0.0500     
Epoch 34/100
60/60 [==============================] - 0s - loss: 23.9928 - dense_3_loss_1: 4.0040 - dense_3_loss_2: 2.8005 - dense_3_loss_3: 1.6887 - dense_3_loss_4: 1.2686 - dense_3_loss_5: 0.9921 - dense_3_loss_6: 0.6885 - dense_3_loss_7: 0.6311 - dense_3_loss_8: 0.5730 - dense_3_loss_9: 0.6141 - dense_3_loss_10: 0.4823 - dense_3_loss_11: 0.5211 - dense_3_loss_12: 0.5289 - dense_3_loss_13: 0.4706 - dense_3_loss_14: 0.4768 - dense_3_loss_15: 0.5065 - dense_3_loss_16: 0.4977 - dense_3_loss_17: 0.5105 - dense_3_loss_18: 0.5055 - dense_3_loss_19: 0.5584 - dense_3_loss_20: 0.5910 - dense_3_loss_21: 0.5747 - dense_3_loss_22: 0.5322 - dense_3_loss_23: 0.5945 - dense_3_loss_24: 0.5149 - dense_3_loss_25: 0.5965 - dense_3_loss_26: 0.5380 - dense_3_loss_27: 0.5617 - dense_3_loss_28: 0.5660 - dense_3_loss_29: 0.6044 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4333 - dense_3_acc_3: 0.6500 - dense_3_acc_4: 0.6667 - dense_3_acc_5: 0.8500 - dense_3_acc_6: 0.9500 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 0.9667 - dense_3_acc_13: 0.9833 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 0.9833 - dense_3_acc_17: 0.9833 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 0.9833 - dense_3_acc_20: 0.9667 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 0.9833 - dense_3_acc_23: 0.9500 - dense_3_acc_24: 0.9500 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9500 - dense_3_acc_29: 0.9167 - dense_3_acc_30: 0.0500     
Epoch 35/100
60/60 [==============================] - 0s - loss: 22.5146 - dense_3_loss_1: 3.9974 - dense_3_loss_2: 2.7475 - dense_3_loss_3: 1.6198 - dense_3_loss_4: 1.1872 - dense_3_loss_5: 0.9220 - dense_3_loss_6: 0.6379 - dense_3_loss_7: 0.5863 - dense_3_loss_8: 0.5155 - dense_3_loss_9: 0.5531 - dense_3_loss_10: 0.4495 - dense_3_loss_11: 0.4776 - dense_3_loss_12: 0.4731 - dense_3_loss_13: 0.4161 - dense_3_loss_14: 0.4440 - dense_3_loss_15: 0.4589 - dense_3_loss_16: 0.4533 - dense_3_loss_17: 0.4462 - dense_3_loss_18: 0.4667 - dense_3_loss_19: 0.5086 - dense_3_loss_20: 0.5256 - dense_3_loss_21: 0.5307 - dense_3_loss_22: 0.4922 - dense_3_loss_23: 0.5267 - dense_3_loss_24: 0.4629 - dense_3_loss_25: 0.5352 - dense_3_loss_26: 0.4865 - dense_3_loss_27: 0.5078 - dense_3_loss_28: 0.5290 - dense_3_loss_29: 0.5573 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4333 - dense_3_acc_3: 0.7000 - dense_3_acc_4: 0.6833 - dense_3_acc_5: 0.8667 - dense_3_acc_6: 0.9500 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 0.9833 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 0.9833 - dense_3_acc_20: 0.9833 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 0.9833 - dense_3_acc_23: 0.9667 - dense_3_acc_24: 0.9667 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9667 - dense_3_acc_29: 0.9333 - dense_3_acc_30: 0.0500     
Epoch 36/100
60/60 [==============================] - 0s - loss: 21.2783 - dense_3_loss_1: 3.9917 - dense_3_loss_2: 2.6965 - dense_3_loss_3: 1.5577 - dense_3_loss_4: 1.1078 - dense_3_loss_5: 0.8617 - dense_3_loss_6: 0.5862 - dense_3_loss_7: 0.5467 - dense_3_loss_8: 0.4654 - dense_3_loss_9: 0.4994 - dense_3_loss_10: 0.4119 - dense_3_loss_11: 0.4452 - dense_3_loss_12: 0.4303 - dense_3_loss_13: 0.3718 - dense_3_loss_14: 0.4132 - dense_3_loss_15: 0.4253 - dense_3_loss_16: 0.4136 - dense_3_loss_17: 0.4015 - dense_3_loss_18: 0.4396 - dense_3_loss_19: 0.4645 - dense_3_loss_20: 0.4769 - dense_3_loss_21: 0.4882 - dense_3_loss_22: 0.4604 - dense_3_loss_23: 0.4726 - dense_3_loss_24: 0.4267 - dense_3_loss_25: 0.4891 - dense_3_loss_26: 0.4475 - dense_3_loss_27: 0.4712 - dense_3_loss_28: 0.4901 - dense_3_loss_29: 0.5259 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4333 - dense_3_acc_3: 0.7000 - dense_3_acc_4: 0.7167 - dense_3_acc_5: 0.8667 - dense_3_acc_6: 0.9667 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 0.9833 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 0.9833 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 0.9833 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 0.9833 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9667 - dense_3_acc_29: 0.9333 - dense_3_acc_30: 0.0500     
Epoch 37/100
60/60 [==============================] - 0s - loss: 20.0358 - dense_3_loss_1: 3.9856 - dense_3_loss_2: 2.6461 - dense_3_loss_3: 1.4957 - dense_3_loss_4: 1.0345 - dense_3_loss_5: 0.8040 - dense_3_loss_6: 0.5428 - dense_3_loss_7: 0.5061 - dense_3_loss_8: 0.4305 - dense_3_loss_9: 0.4580 - dense_3_loss_10: 0.3706 - dense_3_loss_11: 0.4027 - dense_3_loss_12: 0.3881 - dense_3_loss_13: 0.3466 - dense_3_loss_14: 0.3673 - dense_3_loss_15: 0.3870 - dense_3_loss_16: 0.3754 - dense_3_loss_17: 0.3732 - dense_3_loss_18: 0.3834 - dense_3_loss_19: 0.4158 - dense_3_loss_20: 0.4471 - dense_3_loss_21: 0.4459 - dense_3_loss_22: 0.4112 - dense_3_loss_23: 0.4253 - dense_3_loss_24: 0.3880 - dense_3_loss_25: 0.4609 - dense_3_loss_26: 0.4182 - dense_3_loss_27: 0.4162 - dense_3_loss_28: 0.4327 - dense_3_loss_29: 0.4771 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4333 - dense_3_acc_3: 0.7000 - dense_3_acc_4: 0.7333 - dense_3_acc_5: 0.8667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 0.9833 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 0.9833 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 0.9833 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 0.9833 - dense_3_acc_24: 0.9500 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9667 - dense_3_acc_29: 0.9333 - dense_3_acc_30: 0.0500         
Epoch 38/100
60/60 [==============================] - 0s - loss: 18.9407 - dense_3_loss_1: 3.9798 - dense_3_loss_2: 2.5978 - dense_3_loss_3: 1.4376 - dense_3_loss_4: 0.9650 - dense_3_loss_5: 0.7469 - dense_3_loss_6: 0.5009 - dense_3_loss_7: 0.4611 - dense_3_loss_8: 0.4010 - dense_3_loss_9: 0.4190 - dense_3_loss_10: 0.3401 - dense_3_loss_11: 0.3555 - dense_3_loss_12: 0.3529 - dense_3_loss_13: 0.3233 - dense_3_loss_14: 0.3294 - dense_3_loss_15: 0.3551 - dense_3_loss_16: 0.3420 - dense_3_loss_17: 0.3524 - dense_3_loss_18: 0.3470 - dense_3_loss_19: 0.3712 - dense_3_loss_20: 0.4196 - dense_3_loss_21: 0.4107 - dense_3_loss_22: 0.3691 - dense_3_loss_23: 0.3974 - dense_3_loss_24: 0.3445 - dense_3_loss_25: 0.4185 - dense_3_loss_26: 0.3909 - dense_3_loss_27: 0.3780 - dense_3_loss_28: 0.3865 - dense_3_loss_29: 0.4478 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4333 - dense_3_acc_3: 0.7000 - dense_3_acc_4: 0.7833 - dense_3_acc_5: 0.8833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 0.9833 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 0.9667 - dense_3_acc_25: 0.9500 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9833 - dense_3_acc_29: 0.9500 - dense_3_acc_30: 0.0500     
Epoch 39/100
60/60 [==============================] - 0s - loss: 17.8981 - dense_3_loss_1: 3.9741 - dense_3_loss_2: 2.5485 - dense_3_loss_3: 1.3820 - dense_3_loss_4: 0.9083 - dense_3_loss_5: 0.6913 - dense_3_loss_6: 0.4665 - dense_3_loss_7: 0.4254 - dense_3_loss_8: 0.3626 - dense_3_loss_9: 0.3839 - dense_3_loss_10: 0.3056 - dense_3_loss_11: 0.3221 - dense_3_loss_12: 0.3209 - dense_3_loss_13: 0.2887 - dense_3_loss_14: 0.3009 - dense_3_loss_15: 0.3193 - dense_3_loss_16: 0.3055 - dense_3_loss_17: 0.3188 - dense_3_loss_18: 0.3213 - dense_3_loss_19: 0.3316 - dense_3_loss_20: 0.3750 - dense_3_loss_21: 0.3805 - dense_3_loss_22: 0.3467 - dense_3_loss_23: 0.3608 - dense_3_loss_24: 0.3020 - dense_3_loss_25: 0.3727 - dense_3_loss_26: 0.3579 - dense_3_loss_27: 0.3535 - dense_3_loss_28: 0.3573 - dense_3_loss_29: 0.4143 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4667 - dense_3_acc_3: 0.7333 - dense_3_acc_4: 0.8000 - dense_3_acc_5: 0.8833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 0.9833 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9833 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 40/100
60/60 [==============================] - 0s - loss: 16.9639 - dense_3_loss_1: 3.9681 - dense_3_loss_2: 2.5012 - dense_3_loss_3: 1.3292 - dense_3_loss_4: 0.8469 - dense_3_loss_5: 0.6447 - dense_3_loss_6: 0.4319 - dense_3_loss_7: 0.3916 - dense_3_loss_8: 0.3308 - dense_3_loss_9: 0.3516 - dense_3_loss_10: 0.2756 - dense_3_loss_11: 0.2959 - dense_3_loss_12: 0.2953 - dense_3_loss_13: 0.2569 - dense_3_loss_14: 0.2773 - dense_3_loss_15: 0.2842 - dense_3_loss_16: 0.2773 - dense_3_loss_17: 0.2826 - dense_3_loss_18: 0.3013 - dense_3_loss_19: 0.3026 - dense_3_loss_20: 0.3420 - dense_3_loss_21: 0.3481 - dense_3_loss_22: 0.3272 - dense_3_loss_23: 0.3157 - dense_3_loss_24: 0.2828 - dense_3_loss_25: 0.3422 - dense_3_loss_26: 0.3238 - dense_3_loss_27: 0.3255 - dense_3_loss_28: 0.3330 - dense_3_loss_29: 0.3788 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4667 - dense_3_acc_3: 0.7333 - dense_3_acc_4: 0.8500 - dense_3_acc_5: 0.9167 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9667 - dense_3_acc_8: 0.9833 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9833 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 41/100
60/60 [==============================] - 0s - loss: 16.0968 - dense_3_loss_1: 3.9629 - dense_3_loss_2: 2.4537 - dense_3_loss_3: 1.2772 - dense_3_loss_4: 0.7914 - dense_3_loss_5: 0.6029 - dense_3_loss_6: 0.4012 - dense_3_loss_7: 0.3601 - dense_3_loss_8: 0.3020 - dense_3_loss_9: 0.3249 - dense_3_loss_10: 0.2536 - dense_3_loss_11: 0.2672 - dense_3_loss_12: 0.2722 - dense_3_loss_13: 0.2370 - dense_3_loss_14: 0.2512 - dense_3_loss_15: 0.2578 - dense_3_loss_16: 0.2563 - dense_3_loss_17: 0.2587 - dense_3_loss_18: 0.2671 - dense_3_loss_19: 0.2795 - dense_3_loss_20: 0.3193 - dense_3_loss_21: 0.3211 - dense_3_loss_22: 0.2879 - dense_3_loss_23: 0.2854 - dense_3_loss_24: 0.2648 - dense_3_loss_25: 0.3112 - dense_3_loss_26: 0.2973 - dense_3_loss_27: 0.2752 - dense_3_loss_28: 0.3044 - dense_3_loss_29: 0.3533 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4667 - dense_3_acc_3: 0.7500 - dense_3_acc_4: 0.8667 - dense_3_acc_5: 0.9333 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 0.9833 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 0.9833 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 42/100
60/60 [==============================] - 0s - loss: 15.3254 - dense_3_loss_1: 3.9572 - dense_3_loss_2: 2.4098 - dense_3_loss_3: 1.2288 - dense_3_loss_4: 0.7367 - dense_3_loss_5: 0.5634 - dense_3_loss_6: 0.3715 - dense_3_loss_7: 0.3340 - dense_3_loss_8: 0.2812 - dense_3_loss_9: 0.2991 - dense_3_loss_10: 0.2343 - dense_3_loss_11: 0.2444 - dense_3_loss_12: 0.2481 - dense_3_loss_13: 0.2177 - dense_3_loss_14: 0.2325 - dense_3_loss_15: 0.2340 - dense_3_loss_16: 0.2308 - dense_3_loss_17: 0.2380 - dense_3_loss_18: 0.2407 - dense_3_loss_19: 0.2626 - dense_3_loss_20: 0.2941 - dense_3_loss_21: 0.2885 - dense_3_loss_22: 0.2608 - dense_3_loss_23: 0.2625 - dense_3_loss_24: 0.2402 - dense_3_loss_25: 0.2886 - dense_3_loss_26: 0.2645 - dense_3_loss_27: 0.2532 - dense_3_loss_28: 0.2762 - dense_3_loss_29: 0.3318 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4667 - dense_3_acc_3: 0.7667 - dense_3_acc_4: 0.9000 - dense_3_acc_5: 0.9333 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 0.9833 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 43/100
60/60 [==============================] - 0s - loss: 14.6014 - dense_3_loss_1: 3.9526 - dense_3_loss_2: 2.3624 - dense_3_loss_3: 1.1817 - dense_3_loss_4: 0.6865 - dense_3_loss_5: 0.5240 - dense_3_loss_6: 0.3476 - dense_3_loss_7: 0.3075 - dense_3_loss_8: 0.2614 - dense_3_loss_9: 0.2738 - dense_3_loss_10: 0.2116 - dense_3_loss_11: 0.2276 - dense_3_loss_12: 0.2250 - dense_3_loss_13: 0.1994 - dense_3_loss_14: 0.2149 - dense_3_loss_15: 0.2126 - dense_3_loss_16: 0.2096 - dense_3_loss_17: 0.2181 - dense_3_loss_18: 0.2216 - dense_3_loss_19: 0.2370 - dense_3_loss_20: 0.2658 - dense_3_loss_21: 0.2605 - dense_3_loss_22: 0.2451 - dense_3_loss_23: 0.2366 - dense_3_loss_24: 0.2149 - dense_3_loss_25: 0.2647 - dense_3_loss_26: 0.2388 - dense_3_loss_27: 0.2445 - dense_3_loss_28: 0.2552 - dense_3_loss_29: 0.3006 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.4833 - dense_3_acc_3: 0.7833 - dense_3_acc_4: 0.9000 - dense_3_acc_5: 0.9667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 44/100
60/60 [==============================] - 0s - loss: 13.9713 - dense_3_loss_1: 3.9473 - dense_3_loss_2: 2.3194 - dense_3_loss_3: 1.1409 - dense_3_loss_4: 0.6366 - dense_3_loss_5: 0.4887 - dense_3_loss_6: 0.3270 - dense_3_loss_7: 0.2865 - dense_3_loss_8: 0.2406 - dense_3_loss_9: 0.2531 - dense_3_loss_10: 0.1949 - dense_3_loss_11: 0.2071 - dense_3_loss_12: 0.2068 - dense_3_loss_13: 0.1799 - dense_3_loss_14: 0.1970 - dense_3_loss_15: 0.1943 - dense_3_loss_16: 0.1909 - dense_3_loss_17: 0.2018 - dense_3_loss_18: 0.2041 - dense_3_loss_19: 0.2159 - dense_3_loss_20: 0.2424 - dense_3_loss_21: 0.2427 - dense_3_loss_22: 0.2267 - dense_3_loss_23: 0.2183 - dense_3_loss_24: 0.1994 - dense_3_loss_25: 0.2402 - dense_3_loss_26: 0.2237 - dense_3_loss_27: 0.2296 - dense_3_loss_28: 0.2377 - dense_3_loss_29: 0.2780 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.7833 - dense_3_acc_4: 0.9000 - dense_3_acc_5: 0.9667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 45/100
60/60 [==============================] - 0s - loss: 13.3652 - dense_3_loss_1: 3.9433 - dense_3_loss_2: 2.2774 - dense_3_loss_3: 1.0995 - dense_3_loss_4: 0.5960 - dense_3_loss_5: 0.4552 - dense_3_loss_6: 0.3041 - dense_3_loss_7: 0.2641 - dense_3_loss_8: 0.2186 - dense_3_loss_9: 0.2329 - dense_3_loss_10: 0.1809 - dense_3_loss_11: 0.1856 - dense_3_loss_12: 0.1915 - dense_3_loss_13: 0.1652 - dense_3_loss_14: 0.1788 - dense_3_loss_15: 0.1761 - dense_3_loss_16: 0.1772 - dense_3_loss_17: 0.1875 - dense_3_loss_18: 0.1852 - dense_3_loss_19: 0.1953 - dense_3_loss_20: 0.2264 - dense_3_loss_21: 0.2283 - dense_3_loss_22: 0.2034 - dense_3_loss_23: 0.2038 - dense_3_loss_24: 0.1894 - dense_3_loss_25: 0.2208 - dense_3_loss_26: 0.2076 - dense_3_loss_27: 0.1997 - dense_3_loss_28: 0.2143 - dense_3_loss_29: 0.2572 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.7833 - dense_3_acc_4: 0.9000 - dense_3_acc_5: 0.9667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 46/100
60/60 [==============================] - 0s - loss: 12.8271 - dense_3_loss_1: 3.9379 - dense_3_loss_2: 2.2362 - dense_3_loss_3: 1.0628 - dense_3_loss_4: 0.5553 - dense_3_loss_5: 0.4272 - dense_3_loss_6: 0.2835 - dense_3_loss_7: 0.2466 - dense_3_loss_8: 0.2016 - dense_3_loss_9: 0.2149 - dense_3_loss_10: 0.1671 - dense_3_loss_11: 0.1709 - dense_3_loss_12: 0.1780 - dense_3_loss_13: 0.1522 - dense_3_loss_14: 0.1634 - dense_3_loss_15: 0.1620 - dense_3_loss_16: 0.1638 - dense_3_loss_17: 0.1729 - dense_3_loss_18: 0.1709 - dense_3_loss_19: 0.1803 - dense_3_loss_20: 0.2083 - dense_3_loss_21: 0.2106 - dense_3_loss_22: 0.1861 - dense_3_loss_23: 0.1859 - dense_3_loss_24: 0.1727 - dense_3_loss_25: 0.2038 - dense_3_loss_26: 0.1958 - dense_3_loss_27: 0.1816 - dense_3_loss_28: 0.1993 - dense_3_loss_29: 0.2354 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.8000 - dense_3_acc_4: 0.9167 - dense_3_acc_5: 0.9667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500     
Epoch 47/100
60/60 [==============================] - 0s - loss: 12.3347 - dense_3_loss_1: 3.9332 - dense_3_loss_2: 2.1964 - dense_3_loss_3: 1.0276 - dense_3_loss_4: 0.5201 - dense_3_loss_5: 0.4026 - dense_3_loss_6: 0.2653 - dense_3_loss_7: 0.2286 - dense_3_loss_8: 0.1897 - dense_3_loss_9: 0.1969 - dense_3_loss_10: 0.1539 - dense_3_loss_11: 0.1597 - dense_3_loss_12: 0.1632 - dense_3_loss_13: 0.1417 - dense_3_loss_14: 0.1515 - dense_3_loss_15: 0.1499 - dense_3_loss_16: 0.1484 - dense_3_loss_17: 0.1597 - dense_3_loss_18: 0.1586 - dense_3_loss_19: 0.1654 - dense_3_loss_20: 0.1925 - dense_3_loss_21: 0.1909 - dense_3_loss_22: 0.1724 - dense_3_loss_23: 0.1662 - dense_3_loss_24: 0.1550 - dense_3_loss_25: 0.1889 - dense_3_loss_26: 0.1827 - dense_3_loss_27: 0.1677 - dense_3_loss_28: 0.1888 - dense_3_loss_29: 0.2173 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.8000 - dense_3_acc_4: 0.9167 - dense_3_acc_5: 0.9667 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 0.9833 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9667 - dense_3_acc_30: 0.0500         
Epoch 48/100
60/60 [==============================] - 0s - loss: 11.8789 - dense_3_loss_1: 3.9287 - dense_3_loss_2: 2.1570 - dense_3_loss_3: 0.9963 - dense_3_loss_4: 0.4859 - dense_3_loss_5: 0.3775 - dense_3_loss_6: 0.2496 - dense_3_loss_7: 0.2100 - dense_3_loss_8: 0.1784 - dense_3_loss_9: 0.1810 - dense_3_loss_10: 0.1407 - dense_3_loss_11: 0.1485 - dense_3_loss_12: 0.1499 - dense_3_loss_13: 0.1318 - dense_3_loss_14: 0.1395 - dense_3_loss_15: 0.1367 - dense_3_loss_16: 0.1371 - dense_3_loss_17: 0.1457 - dense_3_loss_18: 0.1463 - dense_3_loss_19: 0.1516 - dense_3_loss_20: 0.1804 - dense_3_loss_21: 0.1748 - dense_3_loss_22: 0.1587 - dense_3_loss_23: 0.1532 - dense_3_loss_24: 0.1425 - dense_3_loss_25: 0.1779 - dense_3_loss_26: 0.1654 - dense_3_loss_27: 0.1556 - dense_3_loss_28: 0.1744 - dense_3_loss_29: 0.2036 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.8000 - dense_3_acc_4: 0.9167 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9833 - dense_3_acc_30: 0.0500     
Epoch 49/100
60/60 [==============================] - 0s - loss: 11.4718 - dense_3_loss_1: 3.9246 - dense_3_loss_2: 2.1195 - dense_3_loss_3: 0.9629 - dense_3_loss_4: 0.4579 - dense_3_loss_5: 0.3545 - dense_3_loss_6: 0.2358 - dense_3_loss_7: 0.1950 - dense_3_loss_8: 0.1666 - dense_3_loss_9: 0.1689 - dense_3_loss_10: 0.1302 - dense_3_loss_11: 0.1366 - dense_3_loss_12: 0.1393 - dense_3_loss_13: 0.1209 - dense_3_loss_14: 0.1304 - dense_3_loss_15: 0.1244 - dense_3_loss_16: 0.1280 - dense_3_loss_17: 0.1339 - dense_3_loss_18: 0.1347 - dense_3_loss_19: 0.1417 - dense_3_loss_20: 0.1669 - dense_3_loss_21: 0.1636 - dense_3_loss_22: 0.1456 - dense_3_loss_23: 0.1429 - dense_3_loss_24: 0.1346 - dense_3_loss_25: 0.1642 - dense_3_loss_26: 0.1506 - dense_3_loss_27: 0.1452 - dense_3_loss_28: 0.1612 - dense_3_loss_29: 0.1912 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5000 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9167 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9833 - dense_3_acc_30: 0.0500     
Epoch 50/100
60/60 [==============================] - 0s - loss: 11.0870 - dense_3_loss_1: 3.9202 - dense_3_loss_2: 2.0834 - dense_3_loss_3: 0.9319 - dense_3_loss_4: 0.4288 - dense_3_loss_5: 0.3333 - dense_3_loss_6: 0.2215 - dense_3_loss_7: 0.1811 - dense_3_loss_8: 0.1556 - dense_3_loss_9: 0.1571 - dense_3_loss_10: 0.1221 - dense_3_loss_11: 0.1265 - dense_3_loss_12: 0.1292 - dense_3_loss_13: 0.1117 - dense_3_loss_14: 0.1208 - dense_3_loss_15: 0.1165 - dense_3_loss_16: 0.1193 - dense_3_loss_17: 0.1245 - dense_3_loss_18: 0.1247 - dense_3_loss_19: 0.1318 - dense_3_loss_20: 0.1530 - dense_3_loss_21: 0.1520 - dense_3_loss_22: 0.1359 - dense_3_loss_23: 0.1306 - dense_3_loss_24: 0.1221 - dense_3_loss_25: 0.1504 - dense_3_loss_26: 0.1403 - dense_3_loss_27: 0.1360 - dense_3_loss_28: 0.1497 - dense_3_loss_29: 0.1769 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5167 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9333 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 0.9833 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9833 - dense_3_acc_30: 0.0500     
Epoch 51/100
60/60 [==============================] - 0s - loss: 10.7489 - dense_3_loss_1: 3.9161 - dense_3_loss_2: 2.0472 - dense_3_loss_3: 0.9039 - dense_3_loss_4: 0.4052 - dense_3_loss_5: 0.3150 - dense_3_loss_6: 0.2085 - dense_3_loss_7: 0.1695 - dense_3_loss_8: 0.1464 - dense_3_loss_9: 0.1474 - dense_3_loss_10: 0.1146 - dense_3_loss_11: 0.1173 - dense_3_loss_12: 0.1205 - dense_3_loss_13: 0.1044 - dense_3_loss_14: 0.1121 - dense_3_loss_15: 0.1103 - dense_3_loss_16: 0.1109 - dense_3_loss_17: 0.1167 - dense_3_loss_18: 0.1164 - dense_3_loss_19: 0.1223 - dense_3_loss_20: 0.1403 - dense_3_loss_21: 0.1406 - dense_3_loss_22: 0.1272 - dense_3_loss_23: 0.1202 - dense_3_loss_24: 0.1127 - dense_3_loss_25: 0.1393 - dense_3_loss_26: 0.1325 - dense_3_loss_27: 0.1267 - dense_3_loss_28: 0.1394 - dense_3_loss_29: 0.1653 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5167 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9500 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 0.9833 - dense_3_acc_30: 0.0500     
Epoch 52/100
60/60 [==============================] - 0s - loss: 10.4324 - dense_3_loss_1: 3.9125 - dense_3_loss_2: 2.0145 - dense_3_loss_3: 0.8762 - dense_3_loss_4: 0.3811 - dense_3_loss_5: 0.2983 - dense_3_loss_6: 0.1968 - dense_3_loss_7: 0.1583 - dense_3_loss_8: 0.1365 - dense_3_loss_9: 0.1385 - dense_3_loss_10: 0.1069 - dense_3_loss_11: 0.1099 - dense_3_loss_12: 0.1115 - dense_3_loss_13: 0.0983 - dense_3_loss_14: 0.1046 - dense_3_loss_15: 0.1027 - dense_3_loss_16: 0.1036 - dense_3_loss_17: 0.1093 - dense_3_loss_18: 0.1079 - dense_3_loss_19: 0.1139 - dense_3_loss_20: 0.1320 - dense_3_loss_21: 0.1310 - dense_3_loss_22: 0.1179 - dense_3_loss_23: 0.1121 - dense_3_loss_24: 0.1055 - dense_3_loss_25: 0.1316 - dense_3_loss_26: 0.1227 - dense_3_loss_27: 0.1169 - dense_3_loss_28: 0.1277 - dense_3_loss_29: 0.1541 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5167 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500    
Epoch 53/100
60/60 [==============================] - 0s - loss: 10.1496 - dense_3_loss_1: 3.9084 - dense_3_loss_2: 1.9811 - dense_3_loss_3: 0.8507 - dense_3_loss_4: 0.3591 - dense_3_loss_5: 0.2824 - dense_3_loss_6: 0.1870 - dense_3_loss_7: 0.1495 - dense_3_loss_8: 0.1283 - dense_3_loss_9: 0.1296 - dense_3_loss_10: 0.0997 - dense_3_loss_11: 0.1031 - dense_3_loss_12: 0.1042 - dense_3_loss_13: 0.0918 - dense_3_loss_14: 0.0989 - dense_3_loss_15: 0.0953 - dense_3_loss_16: 0.0968 - dense_3_loss_17: 0.1025 - dense_3_loss_18: 0.1005 - dense_3_loss_19: 0.1063 - dense_3_loss_20: 0.1238 - dense_3_loss_21: 0.1231 - dense_3_loss_22: 0.1083 - dense_3_loss_23: 0.1057 - dense_3_loss_24: 0.0992 - dense_3_loss_25: 0.1226 - dense_3_loss_26: 0.1145 - dense_3_loss_27: 0.1109 - dense_3_loss_28: 0.1207 - dense_3_loss_29: 0.1455 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5167 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 54/100
60/60 [==============================] - 0s - loss: 9.8830 - dense_3_loss_1: 3.9047 - dense_3_loss_2: 1.9502 - dense_3_loss_3: 0.8266 - dense_3_loss_4: 0.3378 - dense_3_loss_5: 0.2663 - dense_3_loss_6: 0.1766 - dense_3_loss_7: 0.1392 - dense_3_loss_8: 0.1211 - dense_3_loss_9: 0.1212 - dense_3_loss_10: 0.0932 - dense_3_loss_11: 0.0970 - dense_3_loss_12: 0.0981 - dense_3_loss_13: 0.0858 - dense_3_loss_14: 0.0928 - dense_3_loss_15: 0.0888 - dense_3_loss_16: 0.0908 - dense_3_loss_17: 0.0961 - dense_3_loss_18: 0.0941 - dense_3_loss_19: 0.0990 - dense_3_loss_20: 0.1170 - dense_3_loss_21: 0.1154 - dense_3_loss_22: 0.1011 - dense_3_loss_23: 0.0983 - dense_3_loss_24: 0.0925 - dense_3_loss_25: 0.1147 - dense_3_loss_26: 0.1073 - dense_3_loss_27: 0.1053 - dense_3_loss_28: 0.1139 - dense_3_loss_29: 0.1381 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0333 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 55/100
60/60 [==============================] - 0s - loss: 9.6414 - dense_3_loss_1: 3.9008 - dense_3_loss_2: 1.9188 - dense_3_loss_3: 0.8036 - dense_3_loss_4: 0.3210 - dense_3_loss_5: 0.2540 - dense_3_loss_6: 0.1686 - dense_3_loss_7: 0.1313 - dense_3_loss_8: 0.1162 - dense_3_loss_9: 0.1149 - dense_3_loss_10: 0.0873 - dense_3_loss_11: 0.0915 - dense_3_loss_12: 0.0923 - dense_3_loss_13: 0.0813 - dense_3_loss_14: 0.0874 - dense_3_loss_15: 0.0832 - dense_3_loss_16: 0.0853 - dense_3_loss_17: 0.0905 - dense_3_loss_18: 0.0878 - dense_3_loss_19: 0.0924 - dense_3_loss_20: 0.1101 - dense_3_loss_21: 0.1080 - dense_3_loss_22: 0.0951 - dense_3_loss_23: 0.0904 - dense_3_loss_24: 0.0866 - dense_3_loss_25: 0.1087 - dense_3_loss_26: 0.1005 - dense_3_loss_27: 0.0982 - dense_3_loss_28: 0.1070 - dense_3_loss_29: 0.1284 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500      
Epoch 56/100
60/60 [==============================] - 0s - loss: 9.4066 - dense_3_loss_1: 3.8973 - dense_3_loss_2: 1.8900 - dense_3_loss_3: 0.7812 - dense_3_loss_4: 0.3040 - dense_3_loss_5: 0.2409 - dense_3_loss_6: 0.1589 - dense_3_loss_7: 0.1230 - dense_3_loss_8: 0.1097 - dense_3_loss_9: 0.1084 - dense_3_loss_10: 0.0824 - dense_3_loss_11: 0.0857 - dense_3_loss_12: 0.0871 - dense_3_loss_13: 0.0763 - dense_3_loss_14: 0.0818 - dense_3_loss_15: 0.0782 - dense_3_loss_16: 0.0803 - dense_3_loss_17: 0.0849 - dense_3_loss_18: 0.0829 - dense_3_loss_19: 0.0868 - dense_3_loss_20: 0.1030 - dense_3_loss_21: 0.1012 - dense_3_loss_22: 0.0896 - dense_3_loss_23: 0.0844 - dense_3_loss_24: 0.0812 - dense_3_loss_25: 0.1021 - dense_3_loss_26: 0.0939 - dense_3_loss_27: 0.0926 - dense_3_loss_28: 0.1002 - dense_3_loss_29: 0.1188 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8167 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 57/100
60/60 [==============================] - 0s - loss: 9.2012 - dense_3_loss_1: 3.8935 - dense_3_loss_2: 1.8605 - dense_3_loss_3: 0.7602 - dense_3_loss_4: 0.2895 - dense_3_loss_5: 0.2301 - dense_3_loss_6: 0.1512 - dense_3_loss_7: 0.1169 - dense_3_loss_8: 0.1028 - dense_3_loss_9: 0.1030 - dense_3_loss_10: 0.0783 - dense_3_loss_11: 0.0808 - dense_3_loss_12: 0.0825 - dense_3_loss_13: 0.0722 - dense_3_loss_14: 0.0775 - dense_3_loss_15: 0.0738 - dense_3_loss_16: 0.0760 - dense_3_loss_17: 0.0791 - dense_3_loss_18: 0.0780 - dense_3_loss_19: 0.0832 - dense_3_loss_20: 0.0958 - dense_3_loss_21: 0.0949 - dense_3_loss_22: 0.0843 - dense_3_loss_23: 0.0808 - dense_3_loss_24: 0.0774 - dense_3_loss_25: 0.0948 - dense_3_loss_26: 0.0884 - dense_3_loss_27: 0.0884 - dense_3_loss_28: 0.0951 - dense_3_loss_29: 0.1121 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8333 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 0.9833 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 58/100
60/60 [==============================] - 0s - loss: 9.0048 - dense_3_loss_1: 3.8899 - dense_3_loss_2: 1.8328 - dense_3_loss_3: 0.7419 - dense_3_loss_4: 0.2746 - dense_3_loss_5: 0.2194 - dense_3_loss_6: 0.1435 - dense_3_loss_7: 0.1109 - dense_3_loss_8: 0.0978 - dense_3_loss_9: 0.0975 - dense_3_loss_10: 0.0742 - dense_3_loss_11: 0.0759 - dense_3_loss_12: 0.0779 - dense_3_loss_13: 0.0684 - dense_3_loss_14: 0.0729 - dense_3_loss_15: 0.0697 - dense_3_loss_16: 0.0719 - dense_3_loss_17: 0.0748 - dense_3_loss_18: 0.0737 - dense_3_loss_19: 0.0783 - dense_3_loss_20: 0.0903 - dense_3_loss_21: 0.0895 - dense_3_loss_22: 0.0788 - dense_3_loss_23: 0.0762 - dense_3_loss_24: 0.0734 - dense_3_loss_25: 0.0895 - dense_3_loss_26: 0.0828 - dense_3_loss_27: 0.0837 - dense_3_loss_28: 0.0898 - dense_3_loss_29: 0.1047 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8333 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 59/100
60/60 [==============================] - 0s - loss: 8.8211 - dense_3_loss_1: 3.8868 - dense_3_loss_2: 1.8068 - dense_3_loss_3: 0.7211 - dense_3_loss_4: 0.2621 - dense_3_loss_5: 0.2090 - dense_3_loss_6: 0.1369 - dense_3_loss_7: 0.1050 - dense_3_loss_8: 0.0934 - dense_3_loss_9: 0.0924 - dense_3_loss_10: 0.0702 - dense_3_loss_11: 0.0718 - dense_3_loss_12: 0.0737 - dense_3_loss_13: 0.0653 - dense_3_loss_14: 0.0691 - dense_3_loss_15: 0.0659 - dense_3_loss_16: 0.0681 - dense_3_loss_17: 0.0707 - dense_3_loss_18: 0.0699 - dense_3_loss_19: 0.0730 - dense_3_loss_20: 0.0857 - dense_3_loss_21: 0.0842 - dense_3_loss_22: 0.0739 - dense_3_loss_23: 0.0717 - dense_3_loss_24: 0.0690 - dense_3_loss_25: 0.0854 - dense_3_loss_26: 0.0770 - dense_3_loss_27: 0.0791 - dense_3_loss_28: 0.0845 - dense_3_loss_29: 0.0992 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8333 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 60/100
60/60 [==============================] - 0s - loss: 8.6520 - dense_3_loss_1: 3.8833 - dense_3_loss_2: 1.7807 - dense_3_loss_3: 0.7037 - dense_3_loss_4: 0.2496 - dense_3_loss_5: 0.1977 - dense_3_loss_6: 0.1301 - dense_3_loss_7: 0.0996 - dense_3_loss_8: 0.0891 - dense_3_loss_9: 0.0872 - dense_3_loss_10: 0.0665 - dense_3_loss_11: 0.0685 - dense_3_loss_12: 0.0700 - dense_3_loss_13: 0.0621 - dense_3_loss_14: 0.0659 - dense_3_loss_15: 0.0624 - dense_3_loss_16: 0.0645 - dense_3_loss_17: 0.0674 - dense_3_loss_18: 0.0663 - dense_3_loss_19: 0.0694 - dense_3_loss_20: 0.0811 - dense_3_loss_21: 0.0796 - dense_3_loss_22: 0.0704 - dense_3_loss_23: 0.0674 - dense_3_loss_24: 0.0648 - dense_3_loss_25: 0.0806 - dense_3_loss_26: 0.0727 - dense_3_loss_27: 0.0758 - dense_3_loss_28: 0.0809 - dense_3_loss_29: 0.0947 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8667 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 0.9833 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 61/100
60/60 [==============================] - 0s - loss: 8.4976 - dense_3_loss_1: 3.8797 - dense_3_loss_2: 1.7566 - dense_3_loss_3: 0.6866 - dense_3_loss_4: 0.2372 - dense_3_loss_5: 0.1897 - dense_3_loss_6: 0.1246 - dense_3_loss_7: 0.0956 - dense_3_loss_8: 0.0850 - dense_3_loss_9: 0.0833 - dense_3_loss_10: 0.0630 - dense_3_loss_11: 0.0656 - dense_3_loss_12: 0.0661 - dense_3_loss_13: 0.0591 - dense_3_loss_14: 0.0631 - dense_3_loss_15: 0.0592 - dense_3_loss_16: 0.0610 - dense_3_loss_17: 0.0639 - dense_3_loss_18: 0.0631 - dense_3_loss_19: 0.0661 - dense_3_loss_20: 0.0762 - dense_3_loss_21: 0.0750 - dense_3_loss_22: 0.0668 - dense_3_loss_23: 0.0635 - dense_3_loss_24: 0.0613 - dense_3_loss_25: 0.0758 - dense_3_loss_26: 0.0692 - dense_3_loss_27: 0.0731 - dense_3_loss_28: 0.0779 - dense_3_loss_29: 0.0903 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8667 - dense_3_acc_4: 0.9833 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 62/100
60/60 [==============================] - 0s - loss: 8.3528 - dense_3_loss_1: 3.8763 - dense_3_loss_2: 1.7313 - dense_3_loss_3: 0.6705 - dense_3_loss_4: 0.2278 - dense_3_loss_5: 0.1811 - dense_3_loss_6: 0.1189 - dense_3_loss_7: 0.0913 - dense_3_loss_8: 0.0813 - dense_3_loss_9: 0.0795 - dense_3_loss_10: 0.0601 - dense_3_loss_11: 0.0629 - dense_3_loss_12: 0.0628 - dense_3_loss_13: 0.0566 - dense_3_loss_14: 0.0603 - dense_3_loss_15: 0.0566 - dense_3_loss_16: 0.0581 - dense_3_loss_17: 0.0611 - dense_3_loss_18: 0.0600 - dense_3_loss_19: 0.0632 - dense_3_loss_20: 0.0726 - dense_3_loss_21: 0.0715 - dense_3_loss_22: 0.0631 - dense_3_loss_23: 0.0606 - dense_3_loss_24: 0.0588 - dense_3_loss_25: 0.0727 - dense_3_loss_26: 0.0656 - dense_3_loss_27: 0.0686 - dense_3_loss_28: 0.0735 - dense_3_loss_29: 0.0860 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 63/100
60/60 [==============================] - 0s - loss: 8.2161 - dense_3_loss_1: 3.8732 - dense_3_loss_2: 1.7085 - dense_3_loss_3: 0.6540 - dense_3_loss_4: 0.2185 - dense_3_loss_5: 0.1729 - dense_3_loss_6: 0.1135 - dense_3_loss_7: 0.0871 - dense_3_loss_8: 0.0782 - dense_3_loss_9: 0.0757 - dense_3_loss_10: 0.0575 - dense_3_loss_11: 0.0602 - dense_3_loss_12: 0.0598 - dense_3_loss_13: 0.0542 - dense_3_loss_14: 0.0575 - dense_3_loss_15: 0.0541 - dense_3_loss_16: 0.0555 - dense_3_loss_17: 0.0583 - dense_3_loss_18: 0.0566 - dense_3_loss_19: 0.0601 - dense_3_loss_20: 0.0696 - dense_3_loss_21: 0.0685 - dense_3_loss_22: 0.0600 - dense_3_loss_23: 0.0577 - dense_3_loss_24: 0.0562 - dense_3_loss_25: 0.0710 - dense_3_loss_26: 0.0625 - dense_3_loss_27: 0.0642 - dense_3_loss_28: 0.0696 - dense_3_loss_29: 0.0813 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5333 - dense_3_acc_3: 0.8667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 64/100
60/60 [==============================] - 0s - loss: 8.0826 - dense_3_loss_1: 3.8697 - dense_3_loss_2: 1.6862 - dense_3_loss_3: 0.6383 - dense_3_loss_4: 0.2101 - dense_3_loss_5: 0.1638 - dense_3_loss_6: 0.1082 - dense_3_loss_7: 0.0833 - dense_3_loss_8: 0.0750 - dense_3_loss_9: 0.0719 - dense_3_loss_10: 0.0552 - dense_3_loss_11: 0.0573 - dense_3_loss_12: 0.0574 - dense_3_loss_13: 0.0515 - dense_3_loss_14: 0.0551 - dense_3_loss_15: 0.0517 - dense_3_loss_16: 0.0531 - dense_3_loss_17: 0.0555 - dense_3_loss_18: 0.0544 - dense_3_loss_19: 0.0572 - dense_3_loss_20: 0.0660 - dense_3_loss_21: 0.0649 - dense_3_loss_22: 0.0574 - dense_3_loss_23: 0.0547 - dense_3_loss_24: 0.0531 - dense_3_loss_25: 0.0659 - dense_3_loss_26: 0.0600 - dense_3_loss_27: 0.0624 - dense_3_loss_28: 0.0669 - dense_3_loss_29: 0.0766 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 65/100
60/60 [==============================] - 0s - loss: 7.9636 - dense_3_loss_1: 3.8666 - dense_3_loss_2: 1.6638 - dense_3_loss_3: 0.6247 - dense_3_loss_4: 0.2023 - dense_3_loss_5: 0.1567 - dense_3_loss_6: 0.1041 - dense_3_loss_7: 0.0799 - dense_3_loss_8: 0.0719 - dense_3_loss_9: 0.0689 - dense_3_loss_10: 0.0529 - dense_3_loss_11: 0.0548 - dense_3_loss_12: 0.0551 - dense_3_loss_13: 0.0493 - dense_3_loss_14: 0.0527 - dense_3_loss_15: 0.0494 - dense_3_loss_16: 0.0510 - dense_3_loss_17: 0.0528 - dense_3_loss_18: 0.0522 - dense_3_loss_19: 0.0548 - dense_3_loss_20: 0.0627 - dense_3_loss_21: 0.0616 - dense_3_loss_22: 0.0552 - dense_3_loss_23: 0.0522 - dense_3_loss_24: 0.0508 - dense_3_loss_25: 0.0618 - dense_3_loss_26: 0.0572 - dense_3_loss_27: 0.0609 - dense_3_loss_28: 0.0645 - dense_3_loss_29: 0.0729 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 66/100
60/60 [==============================] - 0s - loss: 7.8480 - dense_3_loss_1: 3.8634 - dense_3_loss_2: 1.6427 - dense_3_loss_3: 0.6101 - dense_3_loss_4: 0.1947 - dense_3_loss_5: 0.1497 - dense_3_loss_6: 0.1002 - dense_3_loss_7: 0.0766 - dense_3_loss_8: 0.0690 - dense_3_loss_9: 0.0660 - dense_3_loss_10: 0.0505 - dense_3_loss_11: 0.0526 - dense_3_loss_12: 0.0529 - dense_3_loss_13: 0.0474 - dense_3_loss_14: 0.0506 - dense_3_loss_15: 0.0472 - dense_3_loss_16: 0.0490 - dense_3_loss_17: 0.0503 - dense_3_loss_18: 0.0500 - dense_3_loss_19: 0.0523 - dense_3_loss_20: 0.0603 - dense_3_loss_21: 0.0589 - dense_3_loss_22: 0.0526 - dense_3_loss_23: 0.0499 - dense_3_loss_24: 0.0487 - dense_3_loss_25: 0.0602 - dense_3_loss_26: 0.0537 - dense_3_loss_27: 0.0577 - dense_3_loss_28: 0.0610 - dense_3_loss_29: 0.0698 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 67/100
60/60 [==============================] - 0s - loss: 7.7439 - dense_3_loss_1: 3.8600 - dense_3_loss_2: 1.6220 - dense_3_loss_3: 0.5972 - dense_3_loss_4: 0.1879 - dense_3_loss_5: 0.1446 - dense_3_loss_6: 0.0964 - dense_3_loss_7: 0.0739 - dense_3_loss_8: 0.0665 - dense_3_loss_9: 0.0637 - dense_3_loss_10: 0.0486 - dense_3_loss_11: 0.0505 - dense_3_loss_12: 0.0508 - dense_3_loss_13: 0.0458 - dense_3_loss_14: 0.0486 - dense_3_loss_15: 0.0451 - dense_3_loss_16: 0.0470 - dense_3_loss_17: 0.0482 - dense_3_loss_18: 0.0479 - dense_3_loss_19: 0.0501 - dense_3_loss_20: 0.0579 - dense_3_loss_21: 0.0564 - dense_3_loss_22: 0.0501 - dense_3_loss_23: 0.0479 - dense_3_loss_24: 0.0469 - dense_3_loss_25: 0.0582 - dense_3_loss_26: 0.0511 - dense_3_loss_27: 0.0550 - dense_3_loss_28: 0.0587 - dense_3_loss_29: 0.0669 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 68/100
60/60 [==============================] - 0s - loss: 7.6427 - dense_3_loss_1: 3.8569 - dense_3_loss_2: 1.6017 - dense_3_loss_3: 0.5841 - dense_3_loss_4: 0.1813 - dense_3_loss_5: 0.1380 - dense_3_loss_6: 0.0927 - dense_3_loss_7: 0.0712 - dense_3_loss_8: 0.0638 - dense_3_loss_9: 0.0611 - dense_3_loss_10: 0.0469 - dense_3_loss_11: 0.0487 - dense_3_loss_12: 0.0487 - dense_3_loss_13: 0.0441 - dense_3_loss_14: 0.0468 - dense_3_loss_15: 0.0433 - dense_3_loss_16: 0.0451 - dense_3_loss_17: 0.0463 - dense_3_loss_18: 0.0461 - dense_3_loss_19: 0.0485 - dense_3_loss_20: 0.0550 - dense_3_loss_21: 0.0541 - dense_3_loss_22: 0.0481 - dense_3_loss_23: 0.0460 - dense_3_loss_24: 0.0450 - dense_3_loss_25: 0.0550 - dense_3_loss_26: 0.0491 - dense_3_loss_27: 0.0537 - dense_3_loss_28: 0.0567 - dense_3_loss_29: 0.0648 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 69/100
60/60 [==============================] - 0s - loss: 7.5453 - dense_3_loss_1: 3.8538 - dense_3_loss_2: 1.5828 - dense_3_loss_3: 0.5717 - dense_3_loss_4: 0.1752 - dense_3_loss_5: 0.1309 - dense_3_loss_6: 0.0887 - dense_3_loss_7: 0.0688 - dense_3_loss_8: 0.0613 - dense_3_loss_9: 0.0585 - dense_3_loss_10: 0.0454 - dense_3_loss_11: 0.0467 - dense_3_loss_12: 0.0468 - dense_3_loss_13: 0.0425 - dense_3_loss_14: 0.0449 - dense_3_loss_15: 0.0418 - dense_3_loss_16: 0.0434 - dense_3_loss_17: 0.0446 - dense_3_loss_18: 0.0444 - dense_3_loss_19: 0.0466 - dense_3_loss_20: 0.0524 - dense_3_loss_21: 0.0518 - dense_3_loss_22: 0.0464 - dense_3_loss_23: 0.0441 - dense_3_loss_24: 0.0431 - dense_3_loss_25: 0.0524 - dense_3_loss_26: 0.0474 - dense_3_loss_27: 0.0519 - dense_3_loss_28: 0.0549 - dense_3_loss_29: 0.0622 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0667 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 70/100
60/60 [==============================] - 0s - loss: 7.4540 - dense_3_loss_1: 3.8507 - dense_3_loss_2: 1.5633 - dense_3_loss_3: 0.5590 - dense_3_loss_4: 0.1699 - dense_3_loss_5: 0.1252 - dense_3_loss_6: 0.0855 - dense_3_loss_7: 0.0666 - dense_3_loss_8: 0.0592 - dense_3_loss_9: 0.0562 - dense_3_loss_10: 0.0438 - dense_3_loss_11: 0.0450 - dense_3_loss_12: 0.0451 - dense_3_loss_13: 0.0409 - dense_3_loss_14: 0.0434 - dense_3_loss_15: 0.0403 - dense_3_loss_16: 0.0417 - dense_3_loss_17: 0.0431 - dense_3_loss_18: 0.0427 - dense_3_loss_19: 0.0449 - dense_3_loss_20: 0.0504 - dense_3_loss_21: 0.0500 - dense_3_loss_22: 0.0446 - dense_3_loss_23: 0.0422 - dense_3_loss_24: 0.0414 - dense_3_loss_25: 0.0507 - dense_3_loss_26: 0.0458 - dense_3_loss_27: 0.0496 - dense_3_loss_28: 0.0528 - dense_3_loss_29: 0.0599 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 71/100
60/60 [==============================] - 0s - loss: 7.3682 - dense_3_loss_1: 3.8476 - dense_3_loss_2: 1.5457 - dense_3_loss_3: 0.5478 - dense_3_loss_4: 0.1643 - dense_3_loss_5: 0.1198 - dense_3_loss_6: 0.0826 - dense_3_loss_7: 0.0645 - dense_3_loss_8: 0.0572 - dense_3_loss_9: 0.0542 - dense_3_loss_10: 0.0424 - dense_3_loss_11: 0.0435 - dense_3_loss_12: 0.0436 - dense_3_loss_13: 0.0394 - dense_3_loss_14: 0.0420 - dense_3_loss_15: 0.0389 - dense_3_loss_16: 0.0402 - dense_3_loss_17: 0.0416 - dense_3_loss_18: 0.0411 - dense_3_loss_19: 0.0431 - dense_3_loss_20: 0.0487 - dense_3_loss_21: 0.0482 - dense_3_loss_22: 0.0429 - dense_3_loss_23: 0.0407 - dense_3_loss_24: 0.0399 - dense_3_loss_25: 0.0493 - dense_3_loss_26: 0.0441 - dense_3_loss_27: 0.0470 - dense_3_loss_28: 0.0507 - dense_3_loss_29: 0.0574 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 72/100
60/60 [==============================] - 0s - loss: 7.2854 - dense_3_loss_1: 3.8446 - dense_3_loss_2: 1.5274 - dense_3_loss_3: 0.5367 - dense_3_loss_4: 0.1597 - dense_3_loss_5: 0.1145 - dense_3_loss_6: 0.0798 - dense_3_loss_7: 0.0624 - dense_3_loss_8: 0.0552 - dense_3_loss_9: 0.0522 - dense_3_loss_10: 0.0409 - dense_3_loss_11: 0.0419 - dense_3_loss_12: 0.0421 - dense_3_loss_13: 0.0379 - dense_3_loss_14: 0.0406 - dense_3_loss_15: 0.0375 - dense_3_loss_16: 0.0389 - dense_3_loss_17: 0.0400 - dense_3_loss_18: 0.0398 - dense_3_loss_19: 0.0416 - dense_3_loss_20: 0.0469 - dense_3_loss_21: 0.0464 - dense_3_loss_22: 0.0414 - dense_3_loss_23: 0.0392 - dense_3_loss_24: 0.0385 - dense_3_loss_25: 0.0470 - dense_3_loss_26: 0.0424 - dense_3_loss_27: 0.0458 - dense_3_loss_28: 0.0490 - dense_3_loss_29: 0.0550 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 73/100
60/60 [==============================] - 0s - loss: 7.2096 - dense_3_loss_1: 3.8413 - dense_3_loss_2: 1.5103 - dense_3_loss_3: 0.5266 - dense_3_loss_4: 0.1550 - dense_3_loss_5: 0.1105 - dense_3_loss_6: 0.0773 - dense_3_loss_7: 0.0605 - dense_3_loss_8: 0.0534 - dense_3_loss_9: 0.0503 - dense_3_loss_10: 0.0396 - dense_3_loss_11: 0.0405 - dense_3_loss_12: 0.0408 - dense_3_loss_13: 0.0366 - dense_3_loss_14: 0.0392 - dense_3_loss_15: 0.0362 - dense_3_loss_16: 0.0377 - dense_3_loss_17: 0.0384 - dense_3_loss_18: 0.0385 - dense_3_loss_19: 0.0404 - dense_3_loss_20: 0.0452 - dense_3_loss_21: 0.0445 - dense_3_loss_22: 0.0401 - dense_3_loss_23: 0.0379 - dense_3_loss_24: 0.0372 - dense_3_loss_25: 0.0448 - dense_3_loss_26: 0.0410 - dense_3_loss_27: 0.0451 - dense_3_loss_28: 0.0475 - dense_3_loss_29: 0.0530 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 74/100
60/60 [==============================] - 0s - loss: 7.1321 - dense_3_loss_1: 3.8382 - dense_3_loss_2: 1.4937 - dense_3_loss_3: 0.5158 - dense_3_loss_4: 0.1504 - dense_3_loss_5: 0.1045 - dense_3_loss_6: 0.0743 - dense_3_loss_7: 0.0585 - dense_3_loss_8: 0.0515 - dense_3_loss_9: 0.0483 - dense_3_loss_10: 0.0384 - dense_3_loss_11: 0.0392 - dense_3_loss_12: 0.0394 - dense_3_loss_13: 0.0354 - dense_3_loss_14: 0.0378 - dense_3_loss_15: 0.0351 - dense_3_loss_16: 0.0366 - dense_3_loss_17: 0.0371 - dense_3_loss_18: 0.0373 - dense_3_loss_19: 0.0391 - dense_3_loss_20: 0.0436 - dense_3_loss_21: 0.0430 - dense_3_loss_22: 0.0388 - dense_3_loss_23: 0.0366 - dense_3_loss_24: 0.0360 - dense_3_loss_25: 0.0433 - dense_3_loss_26: 0.0395 - dense_3_loss_27: 0.0438 - dense_3_loss_28: 0.0458 - dense_3_loss_29: 0.0513 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 75/100
60/60 [==============================] - 0s - loss: 7.0631 - dense_3_loss_1: 3.8354 - dense_3_loss_2: 1.4772 - dense_3_loss_3: 0.5059 - dense_3_loss_4: 0.1466 - dense_3_loss_5: 0.1002 - dense_3_loss_6: 0.0719 - dense_3_loss_7: 0.0568 - dense_3_loss_8: 0.0501 - dense_3_loss_9: 0.0468 - dense_3_loss_10: 0.0373 - dense_3_loss_11: 0.0380 - dense_3_loss_12: 0.0381 - dense_3_loss_13: 0.0344 - dense_3_loss_14: 0.0367 - dense_3_loss_15: 0.0341 - dense_3_loss_16: 0.0355 - dense_3_loss_17: 0.0360 - dense_3_loss_18: 0.0360 - dense_3_loss_19: 0.0378 - dense_3_loss_20: 0.0423 - dense_3_loss_21: 0.0416 - dense_3_loss_22: 0.0374 - dense_3_loss_23: 0.0354 - dense_3_loss_24: 0.0350 - dense_3_loss_25: 0.0423 - dense_3_loss_26: 0.0382 - dense_3_loss_27: 0.0420 - dense_3_loss_28: 0.0443 - dense_3_loss_29: 0.0499 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 0.9833 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 76/100
60/60 [==============================] - 0s - loss: 6.9951 - dense_3_loss_1: 3.8323 - dense_3_loss_2: 1.4615 - dense_3_loss_3: 0.4957 - dense_3_loss_4: 0.1429 - dense_3_loss_5: 0.0962 - dense_3_loss_6: 0.0695 - dense_3_loss_7: 0.0551 - dense_3_loss_8: 0.0486 - dense_3_loss_9: 0.0453 - dense_3_loss_10: 0.0363 - dense_3_loss_11: 0.0368 - dense_3_loss_12: 0.0369 - dense_3_loss_13: 0.0334 - dense_3_loss_14: 0.0356 - dense_3_loss_15: 0.0330 - dense_3_loss_16: 0.0344 - dense_3_loss_17: 0.0350 - dense_3_loss_18: 0.0347 - dense_3_loss_19: 0.0364 - dense_3_loss_20: 0.0412 - dense_3_loss_21: 0.0404 - dense_3_loss_22: 0.0361 - dense_3_loss_23: 0.0342 - dense_3_loss_24: 0.0338 - dense_3_loss_25: 0.0415 - dense_3_loss_26: 0.0368 - dense_3_loss_27: 0.0400 - dense_3_loss_28: 0.0429 - dense_3_loss_29: 0.0482 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.5667 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 77/100
60/60 [==============================] - 0s - loss: 6.9328 - dense_3_loss_1: 3.8294 - dense_3_loss_2: 1.4463 - dense_3_loss_3: 0.4868 - dense_3_loss_4: 0.1395 - dense_3_loss_5: 0.0932 - dense_3_loss_6: 0.0677 - dense_3_loss_7: 0.0537 - dense_3_loss_8: 0.0475 - dense_3_loss_9: 0.0440 - dense_3_loss_10: 0.0352 - dense_3_loss_11: 0.0358 - dense_3_loss_12: 0.0358 - dense_3_loss_13: 0.0325 - dense_3_loss_14: 0.0346 - dense_3_loss_15: 0.0320 - dense_3_loss_16: 0.0333 - dense_3_loss_17: 0.0339 - dense_3_loss_18: 0.0337 - dense_3_loss_19: 0.0354 - dense_3_loss_20: 0.0396 - dense_3_loss_21: 0.0390 - dense_3_loss_22: 0.0349 - dense_3_loss_23: 0.0331 - dense_3_loss_24: 0.0327 - dense_3_loss_25: 0.0397 - dense_3_loss_26: 0.0357 - dense_3_loss_27: 0.0392 - dense_3_loss_28: 0.0417 - dense_3_loss_29: 0.0469 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 78/100
60/60 [==============================] - 0s - loss: 6.8702 - dense_3_loss_1: 3.8262 - dense_3_loss_2: 1.4312 - dense_3_loss_3: 0.4777 - dense_3_loss_4: 0.1361 - dense_3_loss_5: 0.0894 - dense_3_loss_6: 0.0655 - dense_3_loss_7: 0.0522 - dense_3_loss_8: 0.0459 - dense_3_loss_9: 0.0426 - dense_3_loss_10: 0.0342 - dense_3_loss_11: 0.0347 - dense_3_loss_12: 0.0348 - dense_3_loss_13: 0.0315 - dense_3_loss_14: 0.0337 - dense_3_loss_15: 0.0310 - dense_3_loss_16: 0.0323 - dense_3_loss_17: 0.0329 - dense_3_loss_18: 0.0327 - dense_3_loss_19: 0.0344 - dense_3_loss_20: 0.0382 - dense_3_loss_21: 0.0378 - dense_3_loss_22: 0.0339 - dense_3_loss_23: 0.0322 - dense_3_loss_24: 0.0316 - dense_3_loss_25: 0.0381 - dense_3_loss_26: 0.0347 - dense_3_loss_27: 0.0385 - dense_3_loss_28: 0.0407 - dense_3_loss_29: 0.0455 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 79/100
60/60 [==============================] - 0s - loss: 6.8083 - dense_3_loss_1: 3.8235 - dense_3_loss_2: 1.4166 - dense_3_loss_3: 0.4677 - dense_3_loss_4: 0.1326 - dense_3_loss_5: 0.0851 - dense_3_loss_6: 0.0634 - dense_3_loss_7: 0.0507 - dense_3_loss_8: 0.0444 - dense_3_loss_9: 0.0411 - dense_3_loss_10: 0.0332 - dense_3_loss_11: 0.0337 - dense_3_loss_12: 0.0338 - dense_3_loss_13: 0.0305 - dense_3_loss_14: 0.0327 - dense_3_loss_15: 0.0301 - dense_3_loss_16: 0.0314 - dense_3_loss_17: 0.0319 - dense_3_loss_18: 0.0318 - dense_3_loss_19: 0.0335 - dense_3_loss_20: 0.0370 - dense_3_loss_21: 0.0366 - dense_3_loss_22: 0.0330 - dense_3_loss_23: 0.0313 - dense_3_loss_24: 0.0306 - dense_3_loss_25: 0.0370 - dense_3_loss_26: 0.0337 - dense_3_loss_27: 0.0375 - dense_3_loss_28: 0.0396 - dense_3_loss_29: 0.0442 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 80/100
60/60 [==============================] - 0s - loss: 6.7522 - dense_3_loss_1: 3.8204 - dense_3_loss_2: 1.4023 - dense_3_loss_3: 0.4598 - dense_3_loss_4: 0.1298 - dense_3_loss_5: 0.0826 - dense_3_loss_6: 0.0618 - dense_3_loss_7: 0.0495 - dense_3_loss_8: 0.0432 - dense_3_loss_9: 0.0401 - dense_3_loss_10: 0.0323 - dense_3_loss_11: 0.0327 - dense_3_loss_12: 0.0328 - dense_3_loss_13: 0.0297 - dense_3_loss_14: 0.0318 - dense_3_loss_15: 0.0292 - dense_3_loss_16: 0.0305 - dense_3_loss_17: 0.0309 - dense_3_loss_18: 0.0309 - dense_3_loss_19: 0.0325 - dense_3_loss_20: 0.0360 - dense_3_loss_21: 0.0356 - dense_3_loss_22: 0.0320 - dense_3_loss_23: 0.0303 - dense_3_loss_24: 0.0298 - dense_3_loss_25: 0.0361 - dense_3_loss_26: 0.0324 - dense_3_loss_27: 0.0362 - dense_3_loss_28: 0.0383 - dense_3_loss_29: 0.0425 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 81/100
60/60 [==============================] - 0s - loss: 6.6967 - dense_3_loss_1: 3.8175 - dense_3_loss_2: 1.3884 - dense_3_loss_3: 0.4509 - dense_3_loss_4: 0.1270 - dense_3_loss_5: 0.0789 - dense_3_loss_6: 0.0598 - dense_3_loss_7: 0.0482 - dense_3_loss_8: 0.0418 - dense_3_loss_9: 0.0388 - dense_3_loss_10: 0.0315 - dense_3_loss_11: 0.0318 - dense_3_loss_12: 0.0319 - dense_3_loss_13: 0.0289 - dense_3_loss_14: 0.0309 - dense_3_loss_15: 0.0284 - dense_3_loss_16: 0.0297 - dense_3_loss_17: 0.0300 - dense_3_loss_18: 0.0301 - dense_3_loss_19: 0.0316 - dense_3_loss_20: 0.0350 - dense_3_loss_21: 0.0346 - dense_3_loss_22: 0.0312 - dense_3_loss_23: 0.0295 - dense_3_loss_24: 0.0291 - dense_3_loss_25: 0.0351 - dense_3_loss_26: 0.0316 - dense_3_loss_27: 0.0354 - dense_3_loss_28: 0.0374 - dense_3_loss_29: 0.0414 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 82/100
60/60 [==============================] - 0s - loss: 6.6456 - dense_3_loss_1: 3.8146 - dense_3_loss_2: 1.3752 - dense_3_loss_3: 0.4434 - dense_3_loss_4: 0.1241 - dense_3_loss_5: 0.0768 - dense_3_loss_6: 0.0584 - dense_3_loss_7: 0.0470 - dense_3_loss_8: 0.0408 - dense_3_loss_9: 0.0379 - dense_3_loss_10: 0.0307 - dense_3_loss_11: 0.0309 - dense_3_loss_12: 0.0311 - dense_3_loss_13: 0.0281 - dense_3_loss_14: 0.0301 - dense_3_loss_15: 0.0277 - dense_3_loss_16: 0.0290 - dense_3_loss_17: 0.0292 - dense_3_loss_18: 0.0293 - dense_3_loss_19: 0.0307 - dense_3_loss_20: 0.0340 - dense_3_loss_21: 0.0336 - dense_3_loss_22: 0.0303 - dense_3_loss_23: 0.0286 - dense_3_loss_24: 0.0283 - dense_3_loss_25: 0.0340 - dense_3_loss_26: 0.0306 - dense_3_loss_27: 0.0346 - dense_3_loss_28: 0.0364 - dense_3_loss_29: 0.0403 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 83/100
60/60 [==============================] - 0s - loss: 6.5926 - dense_3_loss_1: 3.8117 - dense_3_loss_2: 1.3613 - dense_3_loss_3: 0.4350 - dense_3_loss_4: 0.1212 - dense_3_loss_5: 0.0737 - dense_3_loss_6: 0.0566 - dense_3_loss_7: 0.0458 - dense_3_loss_8: 0.0395 - dense_3_loss_9: 0.0368 - dense_3_loss_10: 0.0299 - dense_3_loss_11: 0.0301 - dense_3_loss_12: 0.0303 - dense_3_loss_13: 0.0273 - dense_3_loss_14: 0.0292 - dense_3_loss_15: 0.0270 - dense_3_loss_16: 0.0283 - dense_3_loss_17: 0.0284 - dense_3_loss_18: 0.0285 - dense_3_loss_19: 0.0299 - dense_3_loss_20: 0.0331 - dense_3_loss_21: 0.0326 - dense_3_loss_22: 0.0295 - dense_3_loss_23: 0.0278 - dense_3_loss_24: 0.0275 - dense_3_loss_25: 0.0330 - dense_3_loss_26: 0.0298 - dense_3_loss_27: 0.0338 - dense_3_loss_28: 0.0355 - dense_3_loss_29: 0.0392 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.8833 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 84/100
60/60 [==============================] - 0s - loss: 6.5440 - dense_3_loss_1: 3.8089 - dense_3_loss_2: 1.3480 - dense_3_loss_3: 0.4275 - dense_3_loss_4: 0.1187 - dense_3_loss_5: 0.0716 - dense_3_loss_6: 0.0551 - dense_3_loss_7: 0.0447 - dense_3_loss_8: 0.0385 - dense_3_loss_9: 0.0359 - dense_3_loss_10: 0.0292 - dense_3_loss_11: 0.0293 - dense_3_loss_12: 0.0295 - dense_3_loss_13: 0.0266 - dense_3_loss_14: 0.0284 - dense_3_loss_15: 0.0263 - dense_3_loss_16: 0.0276 - dense_3_loss_17: 0.0277 - dense_3_loss_18: 0.0278 - dense_3_loss_19: 0.0291 - dense_3_loss_20: 0.0322 - dense_3_loss_21: 0.0317 - dense_3_loss_22: 0.0287 - dense_3_loss_23: 0.0271 - dense_3_loss_24: 0.0267 - dense_3_loss_25: 0.0322 - dense_3_loss_26: 0.0290 - dense_3_loss_27: 0.0329 - dense_3_loss_28: 0.0345 - dense_3_loss_29: 0.0384 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 85/100
60/60 [==============================] - 0s - loss: 6.4965 - dense_3_loss_1: 3.8059 - dense_3_loss_2: 1.3359 - dense_3_loss_3: 0.4194 - dense_3_loss_4: 0.1162 - dense_3_loss_5: 0.0693 - dense_3_loss_6: 0.0537 - dense_3_loss_7: 0.0436 - dense_3_loss_8: 0.0375 - dense_3_loss_9: 0.0349 - dense_3_loss_10: 0.0284 - dense_3_loss_11: 0.0286 - dense_3_loss_12: 0.0288 - dense_3_loss_13: 0.0260 - dense_3_loss_14: 0.0277 - dense_3_loss_15: 0.0257 - dense_3_loss_16: 0.0269 - dense_3_loss_17: 0.0270 - dense_3_loss_18: 0.0271 - dense_3_loss_19: 0.0284 - dense_3_loss_20: 0.0314 - dense_3_loss_21: 0.0309 - dense_3_loss_22: 0.0280 - dense_3_loss_23: 0.0264 - dense_3_loss_24: 0.0260 - dense_3_loss_25: 0.0315 - dense_3_loss_26: 0.0283 - dense_3_loss_27: 0.0319 - dense_3_loss_28: 0.0337 - dense_3_loss_29: 0.0373 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 86/100
60/60 [==============================] - 0s - loss: 6.4513 - dense_3_loss_1: 3.8033 - dense_3_loss_2: 1.3231 - dense_3_loss_3: 0.4124 - dense_3_loss_4: 0.1138 - dense_3_loss_5: 0.0673 - dense_3_loss_6: 0.0525 - dense_3_loss_7: 0.0426 - dense_3_loss_8: 0.0366 - dense_3_loss_9: 0.0340 - dense_3_loss_10: 0.0277 - dense_3_loss_11: 0.0279 - dense_3_loss_12: 0.0281 - dense_3_loss_13: 0.0253 - dense_3_loss_14: 0.0271 - dense_3_loss_15: 0.0250 - dense_3_loss_16: 0.0263 - dense_3_loss_17: 0.0264 - dense_3_loss_18: 0.0264 - dense_3_loss_19: 0.0277 - dense_3_loss_20: 0.0306 - dense_3_loss_21: 0.0301 - dense_3_loss_22: 0.0273 - dense_3_loss_23: 0.0257 - dense_3_loss_24: 0.0253 - dense_3_loss_25: 0.0307 - dense_3_loss_26: 0.0275 - dense_3_loss_27: 0.0311 - dense_3_loss_28: 0.0329 - dense_3_loss_29: 0.0364 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 87/100
60/60 [==============================] - 0s - loss: 6.4084 - dense_3_loss_1: 3.8002 - dense_3_loss_2: 1.3116 - dense_3_loss_3: 0.4056 - dense_3_loss_4: 0.1116 - dense_3_loss_5: 0.0657 - dense_3_loss_6: 0.0513 - dense_3_loss_7: 0.0417 - dense_3_loss_8: 0.0358 - dense_3_loss_9: 0.0333 - dense_3_loss_10: 0.0271 - dense_3_loss_11: 0.0272 - dense_3_loss_12: 0.0274 - dense_3_loss_13: 0.0247 - dense_3_loss_14: 0.0265 - dense_3_loss_15: 0.0244 - dense_3_loss_16: 0.0256 - dense_3_loss_17: 0.0257 - dense_3_loss_18: 0.0257 - dense_3_loss_19: 0.0270 - dense_3_loss_20: 0.0298 - dense_3_loss_21: 0.0293 - dense_3_loss_22: 0.0266 - dense_3_loss_23: 0.0251 - dense_3_loss_24: 0.0247 - dense_3_loss_25: 0.0298 - dense_3_loss_26: 0.0268 - dense_3_loss_27: 0.0306 - dense_3_loss_28: 0.0320 - dense_3_loss_29: 0.0355 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 88/100
60/60 [==============================] - 0s - loss: 6.3649 - dense_3_loss_1: 3.7973 - dense_3_loss_2: 1.2996 - dense_3_loss_3: 0.3981 - dense_3_loss_4: 0.1096 - dense_3_loss_5: 0.0638 - dense_3_loss_6: 0.0500 - dense_3_loss_7: 0.0407 - dense_3_loss_8: 0.0349 - dense_3_loss_9: 0.0324 - dense_3_loss_10: 0.0265 - dense_3_loss_11: 0.0266 - dense_3_loss_12: 0.0267 - dense_3_loss_13: 0.0241 - dense_3_loss_14: 0.0259 - dense_3_loss_15: 0.0239 - dense_3_loss_16: 0.0250 - dense_3_loss_17: 0.0250 - dense_3_loss_18: 0.0251 - dense_3_loss_19: 0.0264 - dense_3_loss_20: 0.0290 - dense_3_loss_21: 0.0286 - dense_3_loss_22: 0.0259 - dense_3_loss_23: 0.0245 - dense_3_loss_24: 0.0242 - dense_3_loss_25: 0.0290 - dense_3_loss_26: 0.0261 - dense_3_loss_27: 0.0300 - dense_3_loss_28: 0.0313 - dense_3_loss_29: 0.0345 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 89/100
60/60 [==============================] - 0s - loss: 6.3227 - dense_3_loss_1: 3.7945 - dense_3_loss_2: 1.2877 - dense_3_loss_3: 0.3912 - dense_3_loss_4: 0.1071 - dense_3_loss_5: 0.0620 - dense_3_loss_6: 0.0486 - dense_3_loss_7: 0.0398 - dense_3_loss_8: 0.0341 - dense_3_loss_9: 0.0316 - dense_3_loss_10: 0.0259 - dense_3_loss_11: 0.0260 - dense_3_loss_12: 0.0261 - dense_3_loss_13: 0.0236 - dense_3_loss_14: 0.0253 - dense_3_loss_15: 0.0234 - dense_3_loss_16: 0.0245 - dense_3_loss_17: 0.0245 - dense_3_loss_18: 0.0245 - dense_3_loss_19: 0.0258 - dense_3_loss_20: 0.0283 - dense_3_loss_21: 0.0279 - dense_3_loss_22: 0.0253 - dense_3_loss_23: 0.0239 - dense_3_loss_24: 0.0237 - dense_3_loss_25: 0.0284 - dense_3_loss_26: 0.0255 - dense_3_loss_27: 0.0293 - dense_3_loss_28: 0.0306 - dense_3_loss_29: 0.0338 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6167 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 90/100
60/60 [==============================] - 0s - loss: 6.2838 - dense_3_loss_1: 3.7918 - dense_3_loss_2: 1.2768 - dense_3_loss_3: 0.3846 - dense_3_loss_4: 0.1050 - dense_3_loss_5: 0.0607 - dense_3_loss_6: 0.0475 - dense_3_loss_7: 0.0389 - dense_3_loss_8: 0.0334 - dense_3_loss_9: 0.0309 - dense_3_loss_10: 0.0253 - dense_3_loss_11: 0.0254 - dense_3_loss_12: 0.0254 - dense_3_loss_13: 0.0230 - dense_3_loss_14: 0.0248 - dense_3_loss_15: 0.0229 - dense_3_loss_16: 0.0239 - dense_3_loss_17: 0.0239 - dense_3_loss_18: 0.0240 - dense_3_loss_19: 0.0251 - dense_3_loss_20: 0.0277 - dense_3_loss_21: 0.0273 - dense_3_loss_22: 0.0248 - dense_3_loss_23: 0.0233 - dense_3_loss_24: 0.0231 - dense_3_loss_25: 0.0279 - dense_3_loss_26: 0.0249 - dense_3_loss_27: 0.0285 - dense_3_loss_28: 0.0299 - dense_3_loss_29: 0.0330 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 91/100
60/60 [==============================] - 0s - loss: 6.2456 - dense_3_loss_1: 3.7891 - dense_3_loss_2: 1.2658 - dense_3_loss_3: 0.3788 - dense_3_loss_4: 0.1030 - dense_3_loss_5: 0.0591 - dense_3_loss_6: 0.0462 - dense_3_loss_7: 0.0381 - dense_3_loss_8: 0.0326 - dense_3_loss_9: 0.0301 - dense_3_loss_10: 0.0248 - dense_3_loss_11: 0.0248 - dense_3_loss_12: 0.0249 - dense_3_loss_13: 0.0225 - dense_3_loss_14: 0.0242 - dense_3_loss_15: 0.0224 - dense_3_loss_16: 0.0234 - dense_3_loss_17: 0.0233 - dense_3_loss_18: 0.0235 - dense_3_loss_19: 0.0246 - dense_3_loss_20: 0.0271 - dense_3_loss_21: 0.0266 - dense_3_loss_22: 0.0242 - dense_3_loss_23: 0.0228 - dense_3_loss_24: 0.0225 - dense_3_loss_25: 0.0272 - dense_3_loss_26: 0.0243 - dense_3_loss_27: 0.0279 - dense_3_loss_28: 0.0293 - dense_3_loss_29: 0.0324 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 92/100
60/60 [==============================] - 0s - loss: 6.2068 - dense_3_loss_1: 3.7862 - dense_3_loss_2: 1.2549 - dense_3_loss_3: 0.3720 - dense_3_loss_4: 0.1009 - dense_3_loss_5: 0.0578 - dense_3_loss_6: 0.0451 - dense_3_loss_7: 0.0373 - dense_3_loss_8: 0.0319 - dense_3_loss_9: 0.0294 - dense_3_loss_10: 0.0242 - dense_3_loss_11: 0.0243 - dense_3_loss_12: 0.0243 - dense_3_loss_13: 0.0220 - dense_3_loss_14: 0.0236 - dense_3_loss_15: 0.0219 - dense_3_loss_16: 0.0229 - dense_3_loss_17: 0.0228 - dense_3_loss_18: 0.0230 - dense_3_loss_19: 0.0241 - dense_3_loss_20: 0.0264 - dense_3_loss_21: 0.0260 - dense_3_loss_22: 0.0237 - dense_3_loss_23: 0.0222 - dense_3_loss_24: 0.0220 - dense_3_loss_25: 0.0265 - dense_3_loss_26: 0.0238 - dense_3_loss_27: 0.0274 - dense_3_loss_28: 0.0287 - dense_3_loss_29: 0.0316 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 93/100
60/60 [==============================] - 0s - loss: 6.1711 - dense_3_loss_1: 3.7833 - dense_3_loss_2: 1.2445 - dense_3_loss_3: 0.3662 - dense_3_loss_4: 0.0992 - dense_3_loss_5: 0.0565 - dense_3_loss_6: 0.0441 - dense_3_loss_7: 0.0366 - dense_3_loss_8: 0.0312 - dense_3_loss_9: 0.0288 - dense_3_loss_10: 0.0237 - dense_3_loss_11: 0.0238 - dense_3_loss_12: 0.0237 - dense_3_loss_13: 0.0215 - dense_3_loss_14: 0.0232 - dense_3_loss_15: 0.0214 - dense_3_loss_16: 0.0224 - dense_3_loss_17: 0.0223 - dense_3_loss_18: 0.0225 - dense_3_loss_19: 0.0236 - dense_3_loss_20: 0.0257 - dense_3_loss_21: 0.0254 - dense_3_loss_22: 0.0232 - dense_3_loss_23: 0.0218 - dense_3_loss_24: 0.0215 - dense_3_loss_25: 0.0259 - dense_3_loss_26: 0.0233 - dense_3_loss_27: 0.0269 - dense_3_loss_28: 0.0280 - dense_3_loss_29: 0.0309 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 94/100
60/60 [==============================] - 0s - loss: 6.1359 - dense_3_loss_1: 3.7809 - dense_3_loss_2: 1.2339 - dense_3_loss_3: 0.3598 - dense_3_loss_4: 0.0976 - dense_3_loss_5: 0.0553 - dense_3_loss_6: 0.0432 - dense_3_loss_7: 0.0358 - dense_3_loss_8: 0.0305 - dense_3_loss_9: 0.0282 - dense_3_loss_10: 0.0233 - dense_3_loss_11: 0.0233 - dense_3_loss_12: 0.0233 - dense_3_loss_13: 0.0211 - dense_3_loss_14: 0.0227 - dense_3_loss_15: 0.0209 - dense_3_loss_16: 0.0220 - dense_3_loss_17: 0.0219 - dense_3_loss_18: 0.0220 - dense_3_loss_19: 0.0231 - dense_3_loss_20: 0.0252 - dense_3_loss_21: 0.0249 - dense_3_loss_22: 0.0227 - dense_3_loss_23: 0.0213 - dense_3_loss_24: 0.0211 - dense_3_loss_25: 0.0254 - dense_3_loss_26: 0.0227 - dense_3_loss_27: 0.0262 - dense_3_loss_28: 0.0274 - dense_3_loss_29: 0.0303 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 95/100
60/60 [==============================] - 0s - loss: 6.1011 - dense_3_loss_1: 3.7781 - dense_3_loss_2: 1.2239 - dense_3_loss_3: 0.3533 - dense_3_loss_4: 0.0960 - dense_3_loss_5: 0.0540 - dense_3_loss_6: 0.0422 - dense_3_loss_7: 0.0352 - dense_3_loss_8: 0.0298 - dense_3_loss_9: 0.0276 - dense_3_loss_10: 0.0228 - dense_3_loss_11: 0.0228 - dense_3_loss_12: 0.0228 - dense_3_loss_13: 0.0207 - dense_3_loss_14: 0.0223 - dense_3_loss_15: 0.0205 - dense_3_loss_16: 0.0215 - dense_3_loss_17: 0.0214 - dense_3_loss_18: 0.0215 - dense_3_loss_19: 0.0226 - dense_3_loss_20: 0.0247 - dense_3_loss_21: 0.0244 - dense_3_loss_22: 0.0222 - dense_3_loss_23: 0.0208 - dense_3_loss_24: 0.0207 - dense_3_loss_25: 0.0249 - dense_3_loss_26: 0.0222 - dense_3_loss_27: 0.0257 - dense_3_loss_28: 0.0269 - dense_3_loss_29: 0.0297 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 96/100
60/60 [==============================] - 0s - loss: 6.0679 - dense_3_loss_1: 3.7753 - dense_3_loss_2: 1.2137 - dense_3_loss_3: 0.3477 - dense_3_loss_4: 0.0944 - dense_3_loss_5: 0.0531 - dense_3_loss_6: 0.0414 - dense_3_loss_7: 0.0344 - dense_3_loss_8: 0.0293 - dense_3_loss_9: 0.0271 - dense_3_loss_10: 0.0224 - dense_3_loss_11: 0.0223 - dense_3_loss_12: 0.0223 - dense_3_loss_13: 0.0202 - dense_3_loss_14: 0.0220 - dense_3_loss_15: 0.0200 - dense_3_loss_16: 0.0211 - dense_3_loss_17: 0.0210 - dense_3_loss_18: 0.0210 - dense_3_loss_19: 0.0221 - dense_3_loss_20: 0.0241 - dense_3_loss_21: 0.0238 - dense_3_loss_22: 0.0217 - dense_3_loss_23: 0.0204 - dense_3_loss_24: 0.0202 - dense_3_loss_25: 0.0243 - dense_3_loss_26: 0.0217 - dense_3_loss_27: 0.0252 - dense_3_loss_28: 0.0264 - dense_3_loss_29: 0.0291 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 97/100
60/60 [==============================] - 0s - loss: 6.0344 - dense_3_loss_1: 3.7727 - dense_3_loss_2: 1.2036 - dense_3_loss_3: 0.3419 - dense_3_loss_4: 0.0928 - dense_3_loss_5: 0.0518 - dense_3_loss_6: 0.0405 - dense_3_loss_7: 0.0337 - dense_3_loss_8: 0.0287 - dense_3_loss_9: 0.0265 - dense_3_loss_10: 0.0219 - dense_3_loss_11: 0.0218 - dense_3_loss_12: 0.0219 - dense_3_loss_13: 0.0198 - dense_3_loss_14: 0.0215 - dense_3_loss_15: 0.0197 - dense_3_loss_16: 0.0207 - dense_3_loss_17: 0.0205 - dense_3_loss_18: 0.0206 - dense_3_loss_19: 0.0217 - dense_3_loss_20: 0.0236 - dense_3_loss_21: 0.0233 - dense_3_loss_22: 0.0214 - dense_3_loss_23: 0.0199 - dense_3_loss_24: 0.0198 - dense_3_loss_25: 0.0237 - dense_3_loss_26: 0.0213 - dense_3_loss_27: 0.0248 - dense_3_loss_28: 0.0259 - dense_3_loss_29: 0.0285 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 98/100
60/60 [==============================] - 0s - loss: 6.0047 - dense_3_loss_1: 3.7700 - dense_3_loss_2: 1.1947 - dense_3_loss_3: 0.3373 - dense_3_loss_4: 0.0910 - dense_3_loss_5: 0.0508 - dense_3_loss_6: 0.0398 - dense_3_loss_7: 0.0331 - dense_3_loss_8: 0.0281 - dense_3_loss_9: 0.0261 - dense_3_loss_10: 0.0215 - dense_3_loss_11: 0.0214 - dense_3_loss_12: 0.0214 - dense_3_loss_13: 0.0194 - dense_3_loss_14: 0.0211 - dense_3_loss_15: 0.0193 - dense_3_loss_16: 0.0203 - dense_3_loss_17: 0.0201 - dense_3_loss_18: 0.0202 - dense_3_loss_19: 0.0213 - dense_3_loss_20: 0.0231 - dense_3_loss_21: 0.0228 - dense_3_loss_22: 0.0210 - dense_3_loss_23: 0.0195 - dense_3_loss_24: 0.0194 - dense_3_loss_25: 0.0232 - dense_3_loss_26: 0.0209 - dense_3_loss_27: 0.0244 - dense_3_loss_28: 0.0254 - dense_3_loss_29: 0.0280 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 99/100
60/60 [==============================] - 0s - loss: 5.9741 - dense_3_loss_1: 3.7673 - dense_3_loss_2: 1.1851 - dense_3_loss_3: 0.3321 - dense_3_loss_4: 0.0896 - dense_3_loss_5: 0.0499 - dense_3_loss_6: 0.0390 - dense_3_loss_7: 0.0325 - dense_3_loss_8: 0.0276 - dense_3_loss_9: 0.0255 - dense_3_loss_10: 0.0211 - dense_3_loss_11: 0.0210 - dense_3_loss_12: 0.0210 - dense_3_loss_13: 0.0190 - dense_3_loss_14: 0.0206 - dense_3_loss_15: 0.0189 - dense_3_loss_16: 0.0199 - dense_3_loss_17: 0.0197 - dense_3_loss_18: 0.0198 - dense_3_loss_19: 0.0209 - dense_3_loss_20: 0.0227 - dense_3_loss_21: 0.0224 - dense_3_loss_22: 0.0205 - dense_3_loss_23: 0.0191 - dense_3_loss_24: 0.0191 - dense_3_loss_25: 0.0227 - dense_3_loss_26: 0.0206 - dense_3_loss_27: 0.0239 - dense_3_loss_28: 0.0250 - dense_3_loss_29: 0.0275 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6333 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 100/100
60/60 [==============================] - 0s - loss: 5.9443 - dense_3_loss_1: 3.7647 - dense_3_loss_2: 1.1762 - dense_3_loss_3: 0.3267 - dense_3_loss_4: 0.0882 - dense_3_loss_5: 0.0490 - dense_3_loss_6: 0.0382 - dense_3_loss_7: 0.0319 - dense_3_loss_8: 0.0271 - dense_3_loss_9: 0.0250 - dense_3_loss_10: 0.0206 - dense_3_loss_11: 0.0205 - dense_3_loss_12: 0.0206 - dense_3_loss_13: 0.0186 - dense_3_loss_14: 0.0202 - dense_3_loss_15: 0.0186 - dense_3_loss_16: 0.0196 - dense_3_loss_17: 0.0194 - dense_3_loss_18: 0.0194 - dense_3_loss_19: 0.0205 - dense_3_loss_20: 0.0223 - dense_3_loss_21: 0.0220 - dense_3_loss_22: 0.0201 - dense_3_loss_23: 0.0188 - dense_3_loss_24: 0.0188 - dense_3_loss_25: 0.0223 - dense_3_loss_26: 0.0202 - dense_3_loss_27: 0.0234 - dense_3_loss_28: 0.0245 - dense_3_loss_29: 0.0269 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9000 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     

<keras.callbacks.History at 0x7fbc17073c18>

You should see the model loss going down. Now that you have trained a model, lets go on the the final section to implement an inference algorithm, and generate some music!

3 - Generating music

You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music.

3.1 - Predicting & Sampling

At each step of sampling, you will take as input the activation a and cell state c from the previous state of the LSTM, forward propagate by one step, and get a new output activation as well as cell state. The new activation a can then be used to generate the output, using densor as before.

To start off the model, we will initialize x0 as well as the LSTM activation and and cell value a0 and c0 to be zeros.

Exercise: Implement the function below to sample a sequence of musical values. Here are some of the key steps you'll need to implement inside the for-loop that generates the $T_y$ output characters:

Step 2.A: Use LSTM_Cell, which inputs the previous step's c and a to generate the current step's c and a.

Step 2.B: Use densor (defined previously) to compute a softmax on a to get the output for the current step.

Step 2.C: Save the output you have just generated by appending it to outputs.

Step 2.D: Sample x to the be "out"'s one-hot version (the prediction) so that you can pass it to the next LSTM's step. We have already provided this line of code, which uses a Lambda function.

x = Lambda(one_hot)(out) 

[Minor technical note: Rather than sampling a value at random according to the probabilities in out, this line of code actually chooses the single most likely note at each step using an argmax.]

# GRADED FUNCTION: music_inference_model

def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):
    """
    Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.
    
    Arguments:
    LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
    densor -- the trained "densor" from model(), Keras layer object
    n_values -- integer, umber of unique values
    n_a -- number of units in the LSTM_cell
    Ty -- integer, number of time steps to generate
    
    Returns:
    inference_model -- Keras model instance
    """
    
    # Define the input of your model with a shape 
    x0 = Input(shape=(1, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    x = x0

    ### START CODE HERE ###
    # Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
    outputs = []
    
    # Step 2: Loop over Ty and generate a value at every time step
    for t in range(Ty):
        
        # Step 2.A: Perform one step of LSTM_cell (≈1 line)
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        
        # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
        out = densor(a)

        # Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 78) (≈1 line)
        outputs.append(out)
        
        # Step 2.D: Select the next value according to "out", and set "x" to be the one-hot representation of the
        #           selected value, which will be passed as the input to LSTM_cell on the next step. We have provided 
        #           the line of code you need to do this. 
        x = Lambda(one_hot)(out)
        
    # Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
    inference_model = Model(inputs=[x0, a0, c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return inference_model

Run the cell below to define your inference model. This model is hard coded to generate 50 values.

inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)

Finally, this creates the zero-valued vectors you will use to initialize x and the LSTM state variables a and c.

x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))

Exercise: Implement predict_and_sample(). This function takes many arguments including the inputs [x_initializer, a_initializer, c_initializer]. In order to predict the output corresponding to this input, you will need to carry-out 3 steps:

1. Use your inference model to predict an output given your set of inputs. The output pred should be a list of length $T_y$ where each element is a numpy-array of shape (1, n_values).

2. Convert pred into a numpy array of $T_y$ indices. Each index corresponds is computed by taking the argmax of an element of the pred list. Hint.

3. Convert the indices into their one-hot vector representations. Hint.

# GRADED FUNCTION: predict_and_sample

def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, 
                       c_initializer = c_initializer):
    """
    Predicts the next value of values using the inference model.
    
    Arguments:
    inference_model -- Keras model instance for inference time
    x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation
    a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell
    c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel
    
    Returns:
    results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated
    indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated
    """
    
    ### START CODE HERE ###
    # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.
    pred = inference_model.predict([x_initializer, a_initializer, c_initializer])
    # Step 2: Convert "pred" into an np.array() of indices with the maximum probabilities
    indices = np.argmax(pred, axis=-1)
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices, num_classes=78)
    ### END CODE HERE ###
    
    return results, indices

results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
print("np.argmax(results[12]) =", np.argmax(results[12]))
print("np.argmax(results[17]) =", np.argmax(results[17]))
print("list(indices[12:18]) =", list(indices[12:18]))

np.argmax(results[12]) = 58
np.argmax(results[17]) = 19
list(indices[12:18]) = [array([58]), array([19]), array([43]), array([33]), array([58]), array([19])]

Expected Output: Your results may differ because Keras' results are not completely predictable. However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above, you should very likely observe a sequence of indices that are not all identical. Moreover, you should observe that: np.argmax(results[12]) is the first element of list(indices[12:18]) and np.argmax(results[17]) is the last element of list(indices[12:18]).

**np.argmax(results[12])** =	1
**np.argmax(results[17])** =	42
**list(indices[12:18])** =	[array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]

3.3 - Generate music

Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your predict_and_sample() function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time).

Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so lets use it in our implementation as well.

Lets make some music!

Run the following cell to generate music and record it into your out_stream. This can take a couple of minutes.

out_stream = generate_music(inference_model)

Predicting new values for different set of chords.
Generated 51 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("2") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("3") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("4") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("5") and after pruning
Your generated music is saved in output/my_music.midi

To listen to your music, click File->Open... Then go to "output/" and download "my_music.midi". Either play it on your computer with an application that can read midi files if you have one, or use one of the free online "MIDI to mp3" conversion tools to convert this to mp3.

As reference, here also is a 30sec audio clip we generated using this algorithm.

IPython.display.Audio('./data/30s_trained_model.mp3')

Congratulations!

You have come to the end of the notebook.

Here's what you should remember: - A sequence model can be used to generate musical values, which are then post-processed into midi music. - Fairly similar models can be used to generate dinosaur names or to generate music, with the major difference being the input fed to the model. - In Keras, sequence generation involves defining layers with shared weights, which are then repeated for the different time steps $1, \ldots, T_x$.

Congratulations on completing this assignment and generating a jazz solo!

References

The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim's github repository.

• Ji-Sung Kim, 2016, deepjazz

• Jon Gillick, Kevin Tang and Robert Keller, 2009. Learning Jazz Grammars

• Robert Keller and David Morrison, 2007, A Grammatical Approach to Automatic Improvisation

• François Pachet, 1999, Surprising Harmonies

We're also grateful to François Germain for valuable feedback.