Exercise4-Question.ipynb

Kernel: Python 3

Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad. Create a convolutional neural network that trains to 100% accuracy on these images, which cancels training upon hitting training accuracy of >.999

Hint -- it will work best with 3 convolutional layers.

import tensorflow as tf
import os
import zipfile
from os import path, getcwd, chdir

# DO NOT CHANGE THE LINE BELOW. If you are developing in a local
# environment, then grab happy-or-sad.zip from the Coursera Jupyter Notebook
# and place it inside a local folder and edit the path to that location
path = f"{getcwd()}/../tmp2/happy-or-sad.zip"

zip_ref = zipfile.ZipFile(path, 'r')
zip_ref.extractall("/tmp/h-or-s")
zip_ref.close()

# GRADED FUNCTION: train_happy_sad_model
def train_happy_sad_model():
    # Please write your code only where you are indicated.
    # please do not remove # model fitting inline comments.

    DESIRED_ACCURACY = 0.999

    class myCallback(tf.keras.callbacks.Callback):
         def on_epoch_end(self, epoch, logs={}):
                if logs.get('acc') > DESIRED_ACCURACY:
                    print('\nReached 99.9% accuracy so cancelling training!')
                    self.model.stop_training = True

    callbacks = myCallback()
    
    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.
    model = tf.keras.models.Sequential([
        # Your Code Here
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(150, 150, 3)),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

    from tensorflow.keras.optimizers import RMSprop

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
        

    # This code block should create an instance of an ImageDataGenerator called train_datagen 
    # And a train_generator by calling train_datagen.flow_from_directory

    from tensorflow.keras.preprocessing.image import ImageDataGenerator

    train_datagen = ImageDataGenerator(rescale=1/255.0)

    # Please use a target_size of 150 X 150.
    train_generator = train_datagen.flow_from_directory(
        '/tmp/h-or-s',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary'
    )
    # Expected output: 'Found 80 images belonging to 2 classes'

    # This code block should call model.fit_generator and train for
    # a number of epochs.
    # model fitting
    history = model.fit_generator(
        train_generator,
        steps_per_epoch=1,
        epochs=30,
        verbose=1,
        callbacks=[callbacks]
    )
    # model fitting
    return history.history['acc'][-1]

# The Expected output: "Reached 99.9% accuracy so cancelling training!""
train_happy_sad_model()

WARNING: Logging before flag parsing goes to stderr.
W1214 10:41:39.849465 139889356474176 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W1214 10:41:40.224138 139889356474176 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

Found 80 images belonging to 2 classes.
Epoch 1/30
1/1 [==============================] - 5s 5s/step - loss: 0.6968 - acc: 0.5312
Epoch 2/30
1/1 [==============================] - 0s 86ms/step - loss: 1.3948 - acc: 0.4688
Epoch 3/30
1/1 [==============================] - 0s 407ms/step - loss: 0.7137 - acc: 0.5000
Epoch 4/30
1/1 [==============================] - 0s 91ms/step - loss: 0.7384 - acc: 0.4688
Epoch 5/30
1/1 [==============================] - 0s 93ms/step - loss: 0.6834 - acc: 0.5625
Epoch 6/30
1/1 [==============================] - 0s 27ms/step - loss: 0.6924 - acc: 0.4375
Epoch 7/30
1/1 [==============================] - 0s 178ms/step - loss: 0.6829 - acc: 0.5000
Epoch 8/30
1/1 [==============================] - 0s 88ms/step - loss: 0.6716 - acc: 0.5625
Epoch 9/30
1/1 [==============================] - 0s 109ms/step - loss: 0.6962 - acc: 0.4688
Epoch 10/30
1/1 [==============================] - 0s 116ms/step - loss: 0.6600 - acc: 0.7500
Epoch 11/30
1/1 [==============================] - 0s 16ms/step - loss: 0.6318 - acc: 0.6875
Epoch 12/30
1/1 [==============================] - 0s 113ms/step - loss: 0.7155 - acc: 0.4062
Epoch 13/30
1/1 [==============================] - 0s 176ms/step - loss: 0.5984 - acc: 0.5938
Epoch 14/30
1/1 [==============================] - 0s 114ms/step - loss: 0.6052 - acc: 0.8750
Epoch 15/30
1/1 [==============================] - 0s 83ms/step - loss: 0.5532 - acc: 0.8125
Epoch 16/30
1/1 [==============================] - 0s 119ms/step - loss: 0.6283 - acc: 0.5625
Epoch 17/30
1/1 [==============================] - 0s 13ms/step - loss: 0.4351 - acc: 0.9375
Epoch 18/30

Reached 99.9% accuracy so cancelling training!
1/1 [==============================] - 0s 118ms/step - loss: 0.4483 - acc: 1.0000

1.0

# Now click the 'Submit Assignment' button above.
# Once that is complete, please run the following two cells to save your work and close the notebook

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