GitHub Repository: keras-team/keras-io
Path: blob/master/examples/structured_data/md/classification_with_grn_and_vsn.md
³⁵⁰⁸ views

Classification with Gated Residual and Variable Selection Networks

Author: Khalid Salama
Date created: 2021/02/10
Last modified: 2025/01/08
Description: Using Gated Residual and Variable Selection Networks for income level prediction.

View in Colab • GitHub source

Introduction

This example demonstrates the use of Gated Residual Networks (GRN) and Variable Selection Networks (VSN), proposed by Bryan Lim et al. in Temporal Fusion Transformers (TFT) for Interpretable Multi-horizon Time Series Forecasting, for structured data classification. GRNs give the flexibility to the model to apply non-linear processing only where needed. VSNs allow the model to softly remove any unnecessary noisy inputs which could negatively impact performance. Together, those techniques help improving the learning capacity of deep neural network models.

Note that this example implements only the GRN and VSN components described in in the paper, rather than the whole TFT model, as GRN and VSN can be useful on their own for structured data learning tasks.

To run the code you need to use TensorFlow 2.3 or higher.

The dataset

This example uses the United States Census Income Dataset provided by the UC Irvine Machine Learning Repository. The task is binary classification to determine whether a person makes over 50K a year.

The dataset includes ~300K instances with 41 input features: 7 numerical features and 34 categorical features.

Setup

import os
import subprocess
import tarfile

os.environ["KERAS_BACKEND"] = "torch"  # or jax, or tensorflow

import numpy as np
import pandas as pd
import keras
from keras import layers

Prepare the data

First we load the data from the UCI Machine Learning Repository into a Pandas DataFrame.

# Column names.
CSV_HEADER = [
    "age",
    "class_of_worker",
    "detailed_industry_recode",
    "detailed_occupation_recode",
    "education",
    "wage_per_hour",
    "enroll_in_edu_inst_last_wk",
    "marital_stat",
    "major_industry_code",
    "major_occupation_code",
    "race",
    "hispanic_origin",
    "sex",
    "member_of_a_labor_union",
    "reason_for_unemployment",
    "full_or_part_time_employment_stat",
    "capital_gains",
    "capital_losses",
    "dividends_from_stocks",
    "tax_filer_stat",
    "region_of_previous_residence",
    "state_of_previous_residence",
    "detailed_household_and_family_stat",
    "detailed_household_summary_in_household",
    "instance_weight",
    "migration_code-change_in_msa",
    "migration_code-change_in_reg",
    "migration_code-move_within_reg",
    "live_in_this_house_1_year_ago",
    "migration_prev_res_in_sunbelt",
    "num_persons_worked_for_employer",
    "family_members_under_18",
    "country_of_birth_father",
    "country_of_birth_mother",
    "country_of_birth_self",
    "citizenship",
    "own_business_or_self_employed",
    "fill_inc_questionnaire_for_veterans_admin",
    "veterans_benefits",
    "weeks_worked_in_year",
    "year",
    "income_level",
]

data_url = "https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip"
keras.utils.get_file(origin=data_url, extract=True)

``` '/home/humbulani/.keras/datasets/census+income+kdd.zip'

</div>
Determine the downloaded .tar.gz file path and
extract the files from the downloaded .tar.gz file


```python
extracted_path = os.path.join(
    os.path.expanduser("~"), ".keras", "datasets", "census+income+kdd.zip"
)
for root, dirs, files in os.walk(extracted_path):
    for file in files:
        if file.endswith(".tar.gz"):
            tar_gz_path = os.path.join(root, file)
            with tarfile.open(tar_gz_path, "r:gz") as tar:
                tar.extractall(path=root)

train_data_path = os.path.join(
    os.path.expanduser("~"),
    ".keras",
    "datasets",
    "census+income+kdd.zip",
    "census-income.data",
)
test_data_path = os.path.join(
    os.path.expanduser("~"),
    ".keras",
    "datasets",
    "census+income+kdd.zip",
    "census-income.test",
)

data = pd.read_csv(train_data_path, header=None, names=CSV_HEADER)
test_data = pd.read_csv(test_data_path, header=None, names=CSV_HEADER)

print(f"Data shape: {data.shape}")
print(f"Test data shape: {test_data.shape}")

``` Data shape: (199523, 42) Test data shape: (99762, 42)

</div>
We convert the target column from string to integer.


```python
data["income_level"] = data["income_level"].apply(
    lambda x: 0 if x == " - 50000." else 1
)
test_data["income_level"] = test_data["income_level"].apply(
    lambda x: 0 if x == " - 50000." else 1
)

Then, We split the dataset into train and validation sets.

random_selection = np.random.rand(len(data.index)) <= 0.85
train_data = data[random_selection]
valid_data = data[~random_selection]

Finally we store the train and test data splits locally to CSV files.

train_data_file = "train_data.csv"
valid_data_file = "valid_data.csv"
test_data_file = "test_data.csv"

train_data.to_csv(train_data_file, index=False, header=False)
valid_data.to_csv(valid_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)

Define dataset metadata

Here, we define the metadata of the dataset that will be useful for reading and parsing the data into input features, and encoding the input features with respect to their types.

# Target feature name.
TARGET_FEATURE_NAME = "income_level"
# Weight column name.
WEIGHT_COLUMN_NAME = "instance_weight"
# Numeric feature names.
NUMERIC_FEATURE_NAMES = [
    "age",
    "wage_per_hour",
    "capital_gains",
    "capital_losses",
    "dividends_from_stocks",
    "num_persons_worked_for_employer",
    "weeks_worked_in_year",
]
# Categorical features and their vocabulary lists.
# Note that we add 'v=' as a prefix to all categorical feature values to make
# sure that they are treated as strings.
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    feature_name: sorted([str(value) for value in list(data[feature_name].unique())])
    for feature_name in CSV_HEADER
    if feature_name
    not in list(NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME, TARGET_FEATURE_NAME])
}
# All features names.
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list(
    CATEGORICAL_FEATURES_WITH_VOCABULARY.keys()
)
# Feature default values.
COLUMN_DEFAULTS = [
    (
        [0.0]
        if feature_name
        in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME]
        else ["NA"]
    )
    for feature_name in CSV_HEADER
]

Create a `tf.data.Dataset` for training and evaluation

We create an input function to read and parse the file, and convert features and labels into a tf.data.Dataset for training and evaluation.

# Tensorflow required for tf.data.Datasets
import tensorflow as tf


# We process our datasets elements here (categorical) and convert them to indices to avoid this step
# during model training since only tensorflow support strings.
def process(features, target):
    for feature_name in features:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            # Cast categorical feature values to string.
            features[feature_name] = tf.cast(features[feature_name], "string")
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
            # Create a lookup to convert a string values to an integer indices.
            # Since we are not using a mask token nor expecting any out of vocabulary
            # (oov) token, we set mask_token to None and  num_oov_indices to 0.
            index = layers.StringLookup(
                vocabulary=vocabulary,
                mask_token=None,
                num_oov_indices=0,
                output_mode="int",
            )
            # Convert the string input values into integer indices.
            value_index = index(features[feature_name])
            features[feature_name] = value_index
        else:
            # Do nothing for numerical features
            pass

    # Get the instance weight.
    weight = features.pop(WEIGHT_COLUMN_NAME)
    # Change features from OrderedDict to Dict to match Inputs as they are Dict.
    return dict(features), target, weight


def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):
    dataset = tf.data.experimental.make_csv_dataset(
        csv_file_path,
        batch_size=batch_size,
        column_names=CSV_HEADER,
        column_defaults=COLUMN_DEFAULTS,
        label_name=TARGET_FEATURE_NAME,
        num_epochs=1,
        header=False,
        shuffle=shuffle,
    ).map(process)

    return dataset

Create model inputs


def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            # Make them int64, they are Categorical (whole units)
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="int64"
            )
        else:
            # Make them float32, they are Real numbers
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="float32"
            )
    return inputs

Implement the Gated Linear Unit

Gated Linear Units (GLUs) provide the flexibility to suppress input that are not relevant for a given task.


class GatedLinearUnit(layers.Layer):
    def __init__(self, units):
        super().__init__()
        self.linear = layers.Dense(units)
        self.sigmoid = layers.Dense(units, activation="sigmoid")

    def call(self, inputs):
        return self.linear(inputs) * self.sigmoid(inputs)

    # Remove build warnings
    def build(self):
        self.built = True

Implement the Gated Residual Network

The Gated Residual Network (GRN) works as follows:

Applies the nonlinear ELU transformation to the inputs.
Applies linear transformation followed by dropout.
Applies GLU and adds the original inputs to the output of the GLU to perform skip (residual) connection.
Applies layer normalization and produces the output.


class GatedResidualNetwork(layers.Layer):
    def __init__(self, units, dropout_rate):
        super().__init__()
        self.units = units
        self.elu_dense = layers.Dense(units, activation="elu")
        self.linear_dense = layers.Dense(units)
        self.dropout = layers.Dropout(dropout_rate)
        self.gated_linear_unit = GatedLinearUnit(units)
        self.layer_norm = layers.LayerNormalization()
        self.project = layers.Dense(units)

    def call(self, inputs):
        x = self.elu_dense(inputs)
        x = self.linear_dense(x)
        x = self.dropout(x)
        if inputs.shape[-1] != self.units:
            inputs = self.project(inputs)
        x = inputs + self.gated_linear_unit(x)
        x = self.layer_norm(x)
        return x

    # Remove build warnings
    def build(self):
        self.built = True

Implement the Variable Selection Network

The Variable Selection Network (VSN) works as follows:

Applies a GRN to each feature individually.
Applies a GRN on the concatenation of all the features, followed by a softmax to produce feature weights.
Produces a weighted sum of the output of the individual GRN.

Note that the output of the VSN is [batch_size, encoding_size], regardless of the number of the input features.

For categorical features, we encode them using layers.Embedding using the encoding_size as the embedding dimensions. For the numerical features, we apply linear transformation using layers.Dense to project each feature into encoding_size-dimensional vector. Thus, all the encoded features will have the same dimensionality.


class VariableSelection(layers.Layer):
    def __init__(self, num_features, units, dropout_rate):
        super().__init__()
        self.units = units
        # Create an embedding layers with the specified dimensions
        self.embeddings = dict()
        for input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_]
            embedding_encoder = layers.Embedding(
                input_dim=len(vocabulary), output_dim=self.units, name=input_
            )
            self.embeddings[input_] = embedding_encoder

        # Projection layers for numeric features
        self.proj_layer = dict()
        for input_ in NUMERIC_FEATURE_NAMES:
            proj_layer = layers.Dense(units=self.units)
            self.proj_layer[input_] = proj_layer

        self.grns = list()
        # Create a GRN for each feature independently
        for idx in range(num_features):
            grn = GatedResidualNetwork(units, dropout_rate)
            self.grns.append(grn)
        # Create a GRN for the concatenation of all the features
        self.grn_concat = GatedResidualNetwork(units, dropout_rate)
        self.softmax = layers.Dense(units=num_features, activation="softmax")

    def call(self, inputs):
        concat_inputs = []
        for input_ in inputs:
            if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:
                max_index = self.embeddings[input_].input_dim - 1  # Clamp the indices
                # torch had some index errors during embedding hence the clip function
                embedded_feature = self.embeddings[input_](
                    keras.ops.clip(inputs[input_], 0, max_index)
                )
                concat_inputs.append(embedded_feature)
            else:
                # Project the numeric feature to encoding_size using linear transformation.
                proj_feature = keras.ops.expand_dims(inputs[input_], -1)
                proj_feature = self.proj_layer[input_](proj_feature)
                concat_inputs.append(proj_feature)

        v = layers.concatenate(concat_inputs)
        v = self.grn_concat(v)
        v = keras.ops.expand_dims(self.softmax(v), axis=-1)
        x = []
        for idx, input in enumerate(concat_inputs):
            x.append(self.grns[idx](input))
        x = keras.ops.stack(x, axis=1)
        return keras.ops.squeeze(
            keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1
        )

    # to remove the build warnings
    def build(self):
        self.built = True

Create Gated Residual and Variable Selection Networks model


def create_model(encoding_size):
    inputs = create_model_inputs()
    num_features = len(inputs)
    features = VariableSelection(num_features, encoding_size, dropout_rate)(inputs)
    outputs = layers.Dense(units=1, activation="sigmoid")(features)
    # Functional model
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model

Compile, train, and evaluate the model

learning_rate = 0.001
dropout_rate = 0.15
batch_size = 265
num_epochs = 20  # may be adjusted to a desired value
encoding_size = 16

model = create_model(encoding_size)
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
    loss=keras.losses.BinaryCrossentropy(),
    metrics=[keras.metrics.BinaryAccuracy(name="accuracy")],
)

Let's visualize our connectivity graph:

# `rankdir='LR'` is to make the graph horizontal.
keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, rankdir="LR")


# Create an early stopping callback.
early_stopping = keras.callbacks.EarlyStopping(
    monitor="val_loss", patience=5, restore_best_weights=True
)

print("Start training the model...")
train_dataset = get_dataset_from_csv(
    train_data_file, shuffle=True, batch_size=batch_size
)
valid_dataset = get_dataset_from_csv(valid_data_file, batch_size=batch_size)
model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=valid_dataset,
    callbacks=[early_stopping],
)
print("Model training finished.")

print("Evaluating model performance...")
test_dataset = get_dataset_from_csv(test_data_file, batch_size=batch_size)
_, accuracy = model.evaluate(test_dataset)
print(f"Test accuracy: {round(accuracy * 100, 2)}%")

``` Start training the model...

</div>
    
<div class="k-default-codeblock">

1/Unknown 1s 698ms/step - accuracy: 0.4717 - loss: 1212.3043

2/Unknown 1s 200ms/step - accuracy: 0.5745 - loss: 1141.6052

3/Unknown 1s 195ms/step - accuracy: 0.6388 - loss: 1084.4358

4/Unknown 1s 199ms/step - accuracy: 0.6822 - loss: 1031.0354

5/Unknown 2s 201ms/step - accuracy: 0.7131 - loss: 986.4984

6/Unknown 2s 197ms/step - accuracy: 0.7363 - loss: 947.2644

7/Unknown 2s 190ms/step - accuracy: 0.7546 - loss: 912.4213

8/Unknown 2s 188ms/step - accuracy: 0.7698 - loss: 881.4526

9/Unknown 2s 186ms/step - accuracy: 0.7824 - loss: 853.8523

10/Unknown 2s 184ms/step - accuracy: 0.7932 - loss: 829.0496

11/Unknown 3s 183ms/step - accuracy: 0.8022 - loss: 807.4752

12/Unknown 3s 184ms/step - accuracy: 0.8100 - loss: 788.1222

13/Unknown 3s 187ms/step - accuracy: 0.8170 - loss: 770.3723

14/Unknown 3s 187ms/step - accuracy: 0.8233 - loss: 753.6734

15/Unknown 3s 186ms/step - accuracy: 0.8289 - loss: 737.9523

16/Unknown 3s 186ms/step - accuracy: 0.8342 - loss: 723.0760

17/Unknown 4s 186ms/step - accuracy: 0.8389 - loss: 709.2202

18/Unknown 4s 202ms/step - accuracy: 0.8432 - loss: 696.8585

19/Unknown 4s 200ms/step - accuracy: 0.8470 - loss: 685.7762

20/Unknown 4s 198ms/step - accuracy: 0.8505 - loss: 675.3044

21/Unknown 5s 197ms/step - accuracy: 0.8537 - loss: 665.8409

22/Unknown 5s 196ms/step - accuracy: 0.8566 - loss: 657.3629

23/Unknown 5s 195ms/step - accuracy: 0.8593 - loss: 649.5444

24/Unknown 5s 195ms/step - accuracy: 0.8618 - loss: 642.1780

25/Unknown 5s 194ms/step - accuracy: 0.8641 - loss: 635.1900

26/Unknown 6s 195ms/step - accuracy: 0.8662 - loss: 628.5919

27/Unknown 6s 195ms/step - accuracy: 0.8683 - loss: 622.2363

28/Unknown 6s 195ms/step - accuracy: 0.8702 - loss: 616.1565

29/Unknown 6s 194ms/step - accuracy: 0.8720 - loss: 610.3881

30/Unknown 6s 194ms/step - accuracy: 0.8737 - loss: 604.7990

31/Unknown 6s 193ms/step - accuracy: 0.8753 - loss: 599.5613

32/Unknown 7s 194ms/step - accuracy: 0.8769 - loss: 594.4847

33/Unknown 7s 194ms/step - accuracy: 0.8783 - loss: 589.5745

34/Unknown 7s 194ms/step - accuracy: 0.8797 - loss: 584.9431

35/Unknown 7s 194ms/step - accuracy: 0.8810 - loss: 580.5197

36/Unknown 7s 193ms/step - accuracy: 0.8822 - loss: 576.2609

37/Unknown 8s 193ms/step - accuracy: 0.8834 - loss: 572.0708

38/Unknown 8s 194ms/step - accuracy: 0.8845 - loss: 567.9126

39/Unknown 8s 194ms/step - accuracy: 0.8856 - loss: 563.8269

40/Unknown 8s 194ms/step - accuracy: 0.8867 - loss: 559.9911

41/Unknown 8s 194ms/step - accuracy: 0.8877 - loss: 556.2637

42/Unknown 9s 193ms/step - accuracy: 0.8886 - loss: 552.6080

43/Unknown 9s 193ms/step - accuracy: 0.8896 - loss: 549.0726

44/Unknown 9s 193ms/step - accuracy: 0.8905 - loss: 545.6210

45/Unknown 9s 193ms/step - accuracy: 0.8913 - loss: 542.2662

46/Unknown 9s 193ms/step - accuracy: 0.8921 - loss: 539.0649

47/Unknown 10s 193ms/step - accuracy: 0.8929 - loss: 535.9783

48/Unknown 10s 193ms/step - accuracy: 0.8936 - loss: 532.9994

49/Unknown 10s 193ms/step - accuracy: 0.8944 - loss: 530.0856

50/Unknown 10s 193ms/step - accuracy: 0.8951 - loss: 527.2556

51/Unknown 10s 194ms/step - accuracy: 0.8957 - loss: 524.4853

52/Unknown 11s 194ms/step - accuracy: 0.8964 - loss: 521.8221

53/Unknown 11s 194ms/step - accuracy: 0.8970 - loss: 519.2384

54/Unknown 11s 194ms/step - accuracy: 0.8976 - loss: 516.6887

55/Unknown 11s 195ms/step - accuracy: 0.8982 - loss: 514.2283

56/Unknown 11s 195ms/step - accuracy: 0.8987 - loss: 511.8073

57/Unknown 12s 195ms/step - accuracy: 0.8993 - loss: 509.4113

58/Unknown 12s 194ms/step - accuracy: 0.8998 - loss: 507.0705

59/Unknown 12s 194ms/step - accuracy: 0.9004 - loss: 504.7713

60/Unknown 12s 194ms/step - accuracy: 0.9009 - loss: 502.5121

61/Unknown 12s 195ms/step - accuracy: 0.9014 - loss: 500.2973

62/Unknown 13s 195ms/step - accuracy: 0.9019 - loss: 498.1272

63/Unknown 13s 196ms/step - accuracy: 0.9023 - loss: 496.0018

64/Unknown 13s 196ms/step - accuracy: 0.9028 - loss: 493.9293

65/Unknown 13s 197ms/step - accuracy: 0.9032 - loss: 491.9118

66/Unknown 14s 197ms/step - accuracy: 0.9037 - loss: 489.9484

67/Unknown 14s 197ms/step - accuracy: 0.9041 - loss: 488.0164

68/Unknown 14s 197ms/step - accuracy: 0.9045 - loss: 486.1193

69/Unknown 14s 197ms/step - accuracy: 0.9049 - loss: 484.2630

70/Unknown 14s 197ms/step - accuracy: 0.9053 - loss: 482.4265

71/Unknown 14s 197ms/step - accuracy: 0.9057 - loss: 480.6362

72/Unknown 15s 197ms/step - accuracy: 0.9061 - loss: 478.8780

73/Unknown 15s 197ms/step - accuracy: 0.9064 - loss: 477.1625

74/Unknown 15s 197ms/step - accuracy: 0.9068 - loss: 475.4860

75/Unknown 15s 198ms/step - accuracy: 0.9071 - loss: 473.8222

76/Unknown 15s 197ms/step - accuracy: 0.9075 - loss: 472.2155

77/Unknown 16s 197ms/step - accuracy: 0.9078 - loss: 470.6271

78/Unknown 16s 196ms/step - accuracy: 0.9081 - loss: 469.0505

79/Unknown 16s 196ms/step - accuracy: 0.9084 - loss: 467.4939

80/Unknown 16s 196ms/step - accuracy: 0.9087 - loss: 465.9711

81/Unknown 16s 196ms/step - accuracy: 0.9090 - loss: 464.4900

82/Unknown 17s 196ms/step - accuracy: 0.9093 - loss: 463.0288

83/Unknown 17s 196ms/step - accuracy: 0.9096 - loss: 461.5836

84/Unknown 17s 196ms/step - accuracy: 0.9099 - loss: 460.1690

85/Unknown 17s 196ms/step - accuracy: 0.9101 - loss: 458.7745

86/Unknown 17s 196ms/step - accuracy: 0.9104 - loss: 457.3958

87/Unknown 18s 196ms/step - accuracy: 0.9107 - loss: 456.0403

88/Unknown 18s 196ms/step - accuracy: 0.9109 - loss: 454.6969

89/Unknown 18s 195ms/step - accuracy: 0.9112 - loss: 453.3802

90/Unknown 18s 195ms/step - accuracy: 0.9115 - loss: 452.0960

91/Unknown 18s 195ms/step - accuracy: 0.9117 - loss: 450.8279

92/Unknown 18s 195ms/step - accuracy: 0.9119 - loss: 449.5715

93/Unknown 19s 195ms/step - accuracy: 0.9122 - loss: 448.3546

94/Unknown 19s 195ms/step - accuracy: 0.9124 - loss: 447.1467

95/Unknown 19s 195ms/step - accuracy: 0.9126 - loss: 445.9574

96/Unknown 19s 195ms/step - accuracy: 0.9129 - loss: 444.7892

97/Unknown 19s 195ms/step - accuracy: 0.9131 - loss: 443.6310

98/Unknown 20s 195ms/step - accuracy: 0.9133 - loss: 442.5005

99/Unknown 20s 195ms/step - accuracy: 0.9135 - loss: 441.3832

100/Unknown 20s 195ms/step - accuracy: 0.9137 - loss: 440.2770

101/Unknown 20s 194ms/step - accuracy: 0.9139 - loss: 439.1806

102/Unknown 20s 194ms/step - accuracy: 0.9141 - loss: 438.0936

103/Unknown 21s 194ms/step - accuracy: 0.9144 - loss: 437.0181

104/Unknown 21s 195ms/step - accuracy: 0.9146 - loss: 435.9646

105/Unknown 21s 195ms/step - accuracy: 0.9148 - loss: 434.9277

106/Unknown 21s 195ms/step - accuracy: 0.9150 - loss: 433.8949

107/Unknown 21s 195ms/step - accuracy: 0.9151 - loss: 432.8877

108/Unknown 22s 195ms/step - accuracy: 0.9153 - loss: 431.8965

109/Unknown 22s 195ms/step - accuracy: 0.9155 - loss: 430.9133

110/Unknown 22s 196ms/step - accuracy: 0.9157 - loss: 429.9397

111/Unknown 22s 196ms/step - accuracy: 0.9159 - loss: 428.9818

112/Unknown 22s 196ms/step - accuracy: 0.9161 - loss: 428.0353

113/Unknown 23s 197ms/step - accuracy: 0.9162 - loss: 427.0999

114/Unknown 23s 197ms/step - accuracy: 0.9164 - loss: 426.1697

115/Unknown 23s 197ms/step - accuracy: 0.9166 - loss: 425.2458

116/Unknown 23s 197ms/step - accuracy: 0.9168 - loss: 424.3345

117/Unknown 24s 197ms/step - accuracy: 0.9169 - loss: 423.4386

118/Unknown 24s 197ms/step - accuracy: 0.9171 - loss: 422.5567

119/Unknown 24s 197ms/step - accuracy: 0.9173 - loss: 421.6823

120/Unknown 24s 197ms/step - accuracy: 0.9174 - loss: 420.8182

121/Unknown 24s 197ms/step - accuracy: 0.9176 - loss: 419.9664

122/Unknown 25s 197ms/step - accuracy: 0.9177 - loss: 419.1238

123/Unknown 25s 197ms/step - accuracy: 0.9179 - loss: 418.2940

124/Unknown 25s 197ms/step - accuracy: 0.9181 - loss: 417.4785

125/Unknown 25s 197ms/step - accuracy: 0.9182 - loss: 416.6722

126/Unknown 25s 197ms/step - accuracy: 0.9183 - loss: 415.8714

127/Unknown 26s 197ms/step - accuracy: 0.9185 - loss: 415.0771

128/Unknown 26s 197ms/step - accuracy: 0.9186 - loss: 414.2919

129/Unknown 26s 197ms/step - accuracy: 0.9188 - loss: 413.5163

130/Unknown 26s 197ms/step - accuracy: 0.9189 - loss: 412.7452

131/Unknown 26s 198ms/step - accuracy: 0.9191 - loss: 411.9837

132/Unknown 27s 197ms/step - accuracy: 0.9192 - loss: 411.2362

133/Unknown 27s 198ms/step - accuracy: 0.9193 - loss: 410.4987

134/Unknown 27s 198ms/step - accuracy: 0.9195 - loss: 409.7713

135/Unknown 27s 198ms/step - accuracy: 0.9196 - loss: 409.0497

136/Unknown 27s 198ms/step - accuracy: 0.9197 - loss: 408.3427

137/Unknown 28s 198ms/step - accuracy: 0.9199 - loss: 407.6457

138/Unknown 28s 198ms/step - accuracy: 0.9200 - loss: 406.9554

139/Unknown 28s 198ms/step - accuracy: 0.9201 - loss: 406.2739

140/Unknown 28s 198ms/step - accuracy: 0.9202 - loss: 405.6025

141/Unknown 28s 198ms/step - accuracy: 0.9204 - loss: 404.9378

142/Unknown 29s 198ms/step - accuracy: 0.9205 - loss: 404.2800

143/Unknown 29s 198ms/step - accuracy: 0.9206 - loss: 403.6313

144/Unknown 29s 198ms/step - accuracy: 0.9207 - loss: 402.9886

145/Unknown 29s 198ms/step - accuracy: 0.9208 - loss: 402.3509

146/Unknown 29s 198ms/step - accuracy: 0.9209 - loss: 401.7167

147/Unknown 30s 199ms/step - accuracy: 0.9211 - loss: 401.0935

148/Unknown 30s 199ms/step - accuracy: 0.9212 - loss: 400.4745

149/Unknown 30s 200ms/step - accuracy: 0.9213 - loss: 399.8633

150/Unknown 30s 200ms/step - accuracy: 0.9214 - loss: 399.2570

151/Unknown 31s 200ms/step - accuracy: 0.9215 - loss: 398.6591

152/Unknown 31s 200ms/step - accuracy: 0.9216 - loss: 398.0708

153/Unknown 31s 201ms/step - accuracy: 0.9217 - loss: 397.4871

154/Unknown 31s 201ms/step - accuracy: 0.9218 - loss: 396.9082

155/Unknown 32s 201ms/step - accuracy: 0.9219 - loss: 396.3336

156/Unknown 32s 201ms/step - accuracy: 0.9220 - loss: 395.7641

157/Unknown 32s 202ms/step - accuracy: 0.9222 - loss: 395.1993

158/Unknown 32s 202ms/step - accuracy: 0.9223 - loss: 394.6414

159/Unknown 33s 202ms/step - accuracy: 0.9224 - loss: 394.0871

160/Unknown 33s 202ms/step - accuracy: 0.9225 - loss: 393.5349

161/Unknown 33s 202ms/step - accuracy: 0.9226 - loss: 392.9877

162/Unknown 33s 202ms/step - accuracy: 0.9227 - loss: 392.4430

163/Unknown 33s 202ms/step - accuracy: 0.9228 - loss: 391.9062

164/Unknown 34s 202ms/step - accuracy: 0.9229 - loss: 391.3741

165/Unknown 34s 202ms/step - accuracy: 0.9230 - loss: 390.8450

166/Unknown 34s 202ms/step - accuracy: 0.9231 - loss: 390.3184

167/Unknown 34s 202ms/step - accuracy: 0.9232 - loss: 389.7947

168/Unknown 34s 202ms/step - accuracy: 0.9233 - loss: 389.2769

169/Unknown 35s 201ms/step - accuracy: 0.9234 - loss: 388.7642

170/Unknown 35s 201ms/step - accuracy: 0.9234 - loss: 388.2601

171/Unknown 35s 202ms/step - accuracy: 0.9235 - loss: 387.7563

172/Unknown 35s 202ms/step - accuracy: 0.9236 - loss: 387.2565

173/Unknown 35s 202ms/step - accuracy: 0.9237 - loss: 386.7589

174/Unknown 36s 202ms/step - accuracy: 0.9238 - loss: 386.2676

175/Unknown 36s 202ms/step - accuracy: 0.9239 - loss: 385.7783

176/Unknown 36s 202ms/step - accuracy: 0.9240 - loss: 385.2943

177/Unknown 36s 202ms/step - accuracy: 0.9241 - loss: 384.8183

178/Unknown 36s 202ms/step - accuracy: 0.9242 - loss: 384.3459

179/Unknown 37s 202ms/step - accuracy: 0.9243 - loss: 383.8763

180/Unknown 37s 202ms/step - accuracy: 0.9243 - loss: 383.4090

181/Unknown 37s 202ms/step - accuracy: 0.9244 - loss: 382.9447

182/Unknown 37s 202ms/step - accuracy: 0.9245 - loss: 382.4837

183/Unknown 37s 202ms/step - accuracy: 0.9246 - loss: 382.0276

184/Unknown 38s 202ms/step - accuracy: 0.9247 - loss: 381.5741

185/Unknown 38s 202ms/step - accuracy: 0.9248 - loss: 381.1227

186/Unknown 38s 203ms/step - accuracy: 0.9248 - loss: 380.6739

187/Unknown 38s 203ms/step - accuracy: 0.9249 - loss: 380.2263

188/Unknown 39s 203ms/step - accuracy: 0.9250 - loss: 379.7803

189/Unknown 39s 203ms/step - accuracy: 0.9251 - loss: 379.3362

190/Unknown 39s 203ms/step - accuracy: 0.9252 - loss: 378.8958

191/Unknown 39s 203ms/step - accuracy: 0.9252 - loss: 378.4580

192/Unknown 40s 203ms/step - accuracy: 0.9253 - loss: 378.0284

193/Unknown 40s 203ms/step - accuracy: 0.9254 - loss: 377.6024

194/Unknown 40s 203ms/step - accuracy: 0.9255 - loss: 377.1786

195/Unknown 40s 203ms/step - accuracy: 0.9256 - loss: 376.7578

196/Unknown 40s 203ms/step - accuracy: 0.9256 - loss: 376.3396

197/Unknown 41s 203ms/step - accuracy: 0.9257 - loss: 375.9260

198/Unknown 41s 203ms/step - accuracy: 0.9258 - loss: 375.5150

199/Unknown 41s 203ms/step - accuracy: 0.9259 - loss: 375.1063

200/Unknown 41s 203ms/step - accuracy: 0.9259 - loss: 374.6986

201/Unknown 41s 203ms/step - accuracy: 0.9260 - loss: 374.2932

202/Unknown 42s 203ms/step - accuracy: 0.9261 - loss: 373.8907

203/Unknown 42s 203ms/step - accuracy: 0.9262 - loss: 373.4908

204/Unknown 42s 203ms/step - accuracy: 0.9262 - loss: 373.0950

205/Unknown 42s 203ms/step - accuracy: 0.9263 - loss: 372.7011

206/Unknown 42s 203ms/step - accuracy: 0.9264 - loss: 372.3101

207/Unknown 43s 203ms/step - accuracy: 0.9264 - loss: 371.9205

208/Unknown 43s 203ms/step - accuracy: 0.9265 - loss: 371.5334

209/Unknown 43s 203ms/step - accuracy: 0.9266 - loss: 371.1480

210/Unknown 43s 203ms/step - accuracy: 0.9266 - loss: 370.7651

211/Unknown 43s 203ms/step - accuracy: 0.9267 - loss: 370.3852

212/Unknown 44s 203ms/step - accuracy: 0.9268 - loss: 370.0093

213/Unknown 44s 203ms/step - accuracy: 0.9268 - loss: 369.6351

214/Unknown 44s 204ms/step - accuracy: 0.9269 - loss: 369.2646

215/Unknown 44s 204ms/step - accuracy: 0.9270 - loss: 368.8969

216/Unknown 45s 204ms/step - accuracy: 0.9270 - loss: 368.5307

217/Unknown 45s 204ms/step - accuracy: 0.9271 - loss: 368.1656

218/Unknown 45s 204ms/step - accuracy: 0.9272 - loss: 367.8022

219/Unknown 45s 204ms/step - accuracy: 0.9272 - loss: 367.4400

220/Unknown 45s 204ms/step - accuracy: 0.9273 - loss: 367.0780

221/Unknown 46s 204ms/step - accuracy: 0.9274 - loss: 366.7194

222/Unknown 46s 204ms/step - accuracy: 0.9274 - loss: 366.3614

223/Unknown 46s 204ms/step - accuracy: 0.9275 - loss: 366.0057

224/Unknown 46s 205ms/step - accuracy: 0.9275 - loss: 365.6517

225/Unknown 47s 205ms/step - accuracy: 0.9276 - loss: 365.2998

226/Unknown 47s 205ms/step - accuracy: 0.9277 - loss: 364.9511

227/Unknown 47s 205ms/step - accuracy: 0.9277 - loss: 364.6082

228/Unknown 47s 205ms/step - accuracy: 0.9278 - loss: 364.2671

229/Unknown 47s 205ms/step - accuracy: 0.9278 - loss: 363.9267

230/Unknown 48s 205ms/step - accuracy: 0.9279 - loss: 363.5866

231/Unknown 48s 205ms/step - accuracy: 0.9280 - loss: 363.2502

232/Unknown 48s 205ms/step - accuracy: 0.9280 - loss: 362.9175

233/Unknown 48s 205ms/step - accuracy: 0.9281 - loss: 362.5866

234/Unknown 49s 205ms/step - accuracy: 0.9281 - loss: 362.2563

235/Unknown 49s 205ms/step - accuracy: 0.9282 - loss: 361.9282

236/Unknown 49s 206ms/step - accuracy: 0.9283 - loss: 361.6011

237/Unknown 49s 206ms/step - accuracy: 0.9283 - loss: 361.2760

238/Unknown 50s 207ms/step - accuracy: 0.9284 - loss: 360.9536

239/Unknown 50s 207ms/step - accuracy: 0.9284 - loss: 360.6325

240/Unknown 50s 207ms/step - accuracy: 0.9285 - loss: 360.3125

241/Unknown 51s 208ms/step - accuracy: 0.9285 - loss: 359.9926

242/Unknown 51s 208ms/step - accuracy: 0.9286 - loss: 359.6747

243/Unknown 51s 208ms/step - accuracy: 0.9287 - loss: 359.3593

244/Unknown 51s 209ms/step - accuracy: 0.9287 - loss: 359.0458

245/Unknown 52s 209ms/step - accuracy: 0.9288 - loss: 358.7349

246/Unknown 52s 209ms/step - accuracy: 0.9288 - loss: 358.4268

247/Unknown 52s 209ms/step - accuracy: 0.9289 - loss: 358.1215

248/Unknown 52s 209ms/step - accuracy: 0.9289 - loss: 357.8191

249/Unknown 53s 209ms/step - accuracy: 0.9290 - loss: 357.5194

250/Unknown 53s 209ms/step - accuracy: 0.9290 - loss: 357.2212

251/Unknown 53s 209ms/step - accuracy: 0.9291 - loss: 356.9238

252/Unknown 53s 209ms/step - accuracy: 0.9291 - loss: 356.6286

253/Unknown 53s 209ms/step - accuracy: 0.9292 - loss: 356.3355

254/Unknown 54s 210ms/step - accuracy: 0.9292 - loss: 356.0449

255/Unknown 54s 210ms/step - accuracy: 0.9293 - loss: 355.7557

256/Unknown 54s 210ms/step - accuracy: 0.9293 - loss: 355.4673

257/Unknown 54s 210ms/step - accuracy: 0.9294 - loss: 355.1799

258/Unknown 55s 210ms/step - accuracy: 0.9294 - loss: 354.8956

259/Unknown 55s 210ms/step - accuracy: 0.9295 - loss: 354.6136

260/Unknown 55s 210ms/step - accuracy: 0.9295 - loss: 354.3326

261/Unknown 55s 210ms/step - accuracy: 0.9296 - loss: 354.0539

262/Unknown 56s 210ms/step - accuracy: 0.9296 - loss: 353.7773

263/Unknown 56s 210ms/step - accuracy: 0.9297 - loss: 353.5032

264/Unknown 56s 211ms/step - accuracy: 0.9297 - loss: 353.2297

265/Unknown 56s 211ms/step - accuracy: 0.9298 - loss: 352.9581

266/Unknown 57s 211ms/step - accuracy: 0.9298 - loss: 352.6891

267/Unknown 57s 211ms/step - accuracy: 0.9299 - loss: 352.4228

268/Unknown 57s 211ms/step - accuracy: 0.9299 - loss: 352.1577

269/Unknown 57s 212ms/step - accuracy: 0.9300 - loss: 351.8944

270/Unknown 58s 212ms/step - accuracy: 0.9300 - loss: 351.6331

271/Unknown 58s 212ms/step - accuracy: 0.9300 - loss: 351.3750

272/Unknown 58s 212ms/step - accuracy: 0.9301 - loss: 351.1187

273/Unknown 59s 213ms/step - accuracy: 0.9301 - loss: 350.8644

274/Unknown 59s 213ms/step - accuracy: 0.9302 - loss: 350.6121

275/Unknown 59s 213ms/step - accuracy: 0.9302 - loss: 350.3619

276/Unknown 59s 213ms/step - accuracy: 0.9303 - loss: 350.1118

277/Unknown 60s 213ms/step - accuracy: 0.9303 - loss: 349.8630

278/Unknown 60s 213ms/step - accuracy: 0.9303 - loss: 349.6153

279/Unknown 60s 213ms/step - accuracy: 0.9304 - loss: 349.3688

280/Unknown 60s 213ms/step - accuracy: 0.9304 - loss: 349.1229

281/Unknown 60s 213ms/step - accuracy: 0.9305 - loss: 348.8784

282/Unknown 61s 213ms/step - accuracy: 0.9305 - loss: 348.6351

283/Unknown 61s 213ms/step - accuracy: 0.9306 - loss: 348.3920

284/Unknown 61s 213ms/step - accuracy: 0.9306 - loss: 348.1502

285/Unknown 61s 214ms/step - accuracy: 0.9306 - loss: 347.9100

286/Unknown 62s 214ms/step - accuracy: 0.9307 - loss: 347.6703

287/Unknown 62s 214ms/step - accuracy: 0.9307 - loss: 347.4321

288/Unknown 62s 214ms/step - accuracy: 0.9308 - loss: 347.1949

289/Unknown 62s 214ms/step - accuracy: 0.9308 - loss: 346.9582

290/Unknown 63s 214ms/step - accuracy: 0.9308 - loss: 346.7231

291/Unknown 63s 214ms/step - accuracy: 0.9309 - loss: 346.4895

292/Unknown 63s 214ms/step - accuracy: 0.9309 - loss: 346.2580

293/Unknown 63s 214ms/step - accuracy: 0.9310 - loss: 346.0265

294/Unknown 63s 214ms/step - accuracy: 0.9310 - loss: 345.7953

295/Unknown 64s 214ms/step - accuracy: 0.9310 - loss: 345.5652

296/Unknown 64s 214ms/step - accuracy: 0.9311 - loss: 345.3354

297/Unknown 64s 214ms/step - accuracy: 0.9311 - loss: 345.1051

298/Unknown 64s 214ms/step - accuracy: 0.9312 - loss: 344.8757

299/Unknown 65s 214ms/step - accuracy: 0.9312 - loss: 344.6488

300/Unknown 65s 215ms/step - accuracy: 0.9312 - loss: 344.4228

301/Unknown 65s 215ms/step - accuracy: 0.9313 - loss: 344.1973

302/Unknown 65s 215ms/step - accuracy: 0.9313 - loss: 343.9730

303/Unknown 66s 215ms/step - accuracy: 0.9314 - loss: 343.7490

304/Unknown 66s 215ms/step - accuracy: 0.9314 - loss: 343.5257

305/Unknown 66s 215ms/step - accuracy: 0.9314 - loss: 343.3047

306/Unknown 66s 215ms/step - accuracy: 0.9315 - loss: 343.0848

307/Unknown 66s 215ms/step - accuracy: 0.9315 - loss: 342.8660

308/Unknown 67s 215ms/step - accuracy: 0.9315 - loss: 342.6490

309/Unknown 67s 215ms/step - accuracy: 0.9316 - loss: 342.4344

310/Unknown 67s 215ms/step - accuracy: 0.9316 - loss: 342.2202

311/Unknown 67s 215ms/step - accuracy: 0.9316 - loss: 342.0071

312/Unknown 67s 215ms/step - accuracy: 0.9317 - loss: 341.7946

313/Unknown 68s 215ms/step - accuracy: 0.9317 - loss: 341.5840

314/Unknown 68s 215ms/step - accuracy: 0.9318 - loss: 341.3745

315/Unknown 68s 215ms/step - accuracy: 0.9318 - loss: 341.1663

316/Unknown 68s 215ms/step - accuracy: 0.9318 - loss: 340.9592

317/Unknown 69s 215ms/step - accuracy: 0.9319 - loss: 340.7528

318/Unknown 69s 215ms/step - accuracy: 0.9319 - loss: 340.5479

319/Unknown 69s 215ms/step - accuracy: 0.9319 - loss: 340.3434

320/Unknown 69s 215ms/step - accuracy: 0.9320 - loss: 340.1393

321/Unknown 69s 215ms/step - accuracy: 0.9320 - loss: 339.9351

322/Unknown 70s 215ms/step - accuracy: 0.9320 - loss: 339.7322

323/Unknown 70s 214ms/step - accuracy: 0.9321 - loss: 339.5305

324/Unknown 70s 214ms/step - accuracy: 0.9321 - loss: 339.3291

325/Unknown 70s 214ms/step - accuracy: 0.9321 - loss: 339.1288

326/Unknown 70s 214ms/step - accuracy: 0.9322 - loss: 338.9296

327/Unknown 71s 214ms/step - accuracy: 0.9322 - loss: 338.7308

328/Unknown 71s 214ms/step - accuracy: 0.9322 - loss: 338.5333

329/Unknown 71s 214ms/step - accuracy: 0.9323 - loss: 338.3367

330/Unknown 71s 214ms/step - accuracy: 0.9323 - loss: 338.1408

331/Unknown 71s 214ms/step - accuracy: 0.9323 - loss: 337.9453

332/Unknown 71s 214ms/step - accuracy: 0.9324 - loss: 337.7515

333/Unknown 72s 214ms/step - accuracy: 0.9324 - loss: 337.5584

334/Unknown 72s 214ms/step - accuracy: 0.9324 - loss: 337.3662

335/Unknown 72s 214ms/step - accuracy: 0.9325 - loss: 337.1747

336/Unknown 72s 214ms/step - accuracy: 0.9325 - loss: 336.9847

337/Unknown 72s 214ms/step - accuracy: 0.9325 - loss: 336.7955

338/Unknown 73s 214ms/step - accuracy: 0.9326 - loss: 336.6066

339/Unknown 73s 214ms/step - accuracy: 0.9326 - loss: 336.4176

340/Unknown 73s 214ms/step - accuracy: 0.9326 - loss: 336.2293

341/Unknown 73s 214ms/step - accuracy: 0.9327 - loss: 336.0417

342/Unknown 74s 214ms/step - accuracy: 0.9327 - loss: 335.8549

343/Unknown 74s 213ms/step - accuracy: 0.9327 - loss: 335.6696

344/Unknown 74s 213ms/step - accuracy: 0.9328 - loss: 335.4847

345/Unknown 74s 213ms/step - accuracy: 0.9328 - loss: 335.3005

346/Unknown 74s 214ms/step - accuracy: 0.9328 - loss: 335.1162

347/Unknown 75s 214ms/step - accuracy: 0.9328 - loss: 334.9317

348/Unknown 75s 214ms/step - accuracy: 0.9329 - loss: 334.7472

349/Unknown 75s 214ms/step - accuracy: 0.9329 - loss: 334.5628

350/Unknown 75s 214ms/step - accuracy: 0.9329 - loss: 334.3791

351/Unknown 75s 214ms/step - accuracy: 0.9330 - loss: 334.1966

352/Unknown 76s 214ms/step - accuracy: 0.9330 - loss: 334.0144

353/Unknown 76s 214ms/step - accuracy: 0.9330 - loss: 333.8329

354/Unknown 76s 214ms/step - accuracy: 0.9331 - loss: 333.6525

355/Unknown 76s 214ms/step - accuracy: 0.9331 - loss: 333.4737

356/Unknown 77s 214ms/step - accuracy: 0.9331 - loss: 333.2956

357/Unknown 77s 214ms/step - accuracy: 0.9332 - loss: 333.1186

358/Unknown 77s 214ms/step - accuracy: 0.9332 - loss: 332.9418

359/Unknown 77s 214ms/step - accuracy: 0.9332 - loss: 332.7658

360/Unknown 77s 214ms/step - accuracy: 0.9332 - loss: 332.5904

361/Unknown 78s 214ms/step - accuracy: 0.9333 - loss: 332.4163

362/Unknown 78s 214ms/step - accuracy: 0.9333 - loss: 332.2424

363/Unknown 78s 214ms/step - accuracy: 0.9333 - loss: 332.0692

364/Unknown 78s 214ms/step - accuracy: 0.9334 - loss: 331.8963

365/Unknown 79s 214ms/step - accuracy: 0.9334 - loss: 331.7242

366/Unknown 79s 214ms/step - accuracy: 0.9334 - loss: 331.5526

367/Unknown 79s 214ms/step - accuracy: 0.9334 - loss: 331.3815

368/Unknown 79s 214ms/step - accuracy: 0.9335 - loss: 331.2112

369/Unknown 79s 214ms/step - accuracy: 0.9335 - loss: 331.0415

370/Unknown 80s 214ms/step - accuracy: 0.9335 - loss: 330.8718

371/Unknown 80s 214ms/step - accuracy: 0.9336 - loss: 330.7039

372/Unknown 80s 214ms/step - accuracy: 0.9336 - loss: 330.5366

373/Unknown 80s 214ms/step - accuracy: 0.9336 - loss: 330.3701

374/Unknown 81s 214ms/step - accuracy: 0.9336 - loss: 330.2039

375/Unknown 81s 214ms/step - accuracy: 0.9337 - loss: 330.0381

376/Unknown 81s 214ms/step - accuracy: 0.9337 - loss: 329.8729

377/Unknown 81s 214ms/step - accuracy: 0.9337 - loss: 329.7084

378/Unknown 81s 214ms/step - accuracy: 0.9338 - loss: 329.5450

379/Unknown 82s 214ms/step - accuracy: 0.9338 - loss: 329.3824

380/Unknown 82s 214ms/step - accuracy: 0.9338 - loss: 329.2206

381/Unknown 82s 214ms/step - accuracy: 0.9338 - loss: 329.0592

382/Unknown 82s 214ms/step - accuracy: 0.9339 - loss: 328.8988

383/Unknown 83s 214ms/step - accuracy: 0.9339 - loss: 328.7392

384/Unknown 83s 214ms/step - accuracy: 0.9339 - loss: 328.5800

385/Unknown 83s 214ms/step - accuracy: 0.9339 - loss: 328.4217

386/Unknown 83s 214ms/step - accuracy: 0.9340 - loss: 328.2640

387/Unknown 84s 215ms/step - accuracy: 0.9340 - loss: 328.1079

388/Unknown 84s 215ms/step - accuracy: 0.9340 - loss: 327.9520

389/Unknown 84s 215ms/step - accuracy: 0.9340 - loss: 327.7965

390/Unknown 84s 215ms/step - accuracy: 0.9341 - loss: 327.6415

391/Unknown 84s 215ms/step - accuracy: 0.9341 - loss: 327.4871

392/Unknown 85s 215ms/step - accuracy: 0.9341 - loss: 327.3329

393/Unknown 85s 215ms/step - accuracy: 0.9341 - loss: 327.1793

394/Unknown 85s 215ms/step - accuracy: 0.9342 - loss: 327.0259

395/Unknown 85s 215ms/step - accuracy: 0.9342 - loss: 326.8732

396/Unknown 86s 215ms/step - accuracy: 0.9342 - loss: 326.7209

397/Unknown 86s 215ms/step - accuracy: 0.9342 - loss: 326.5693

398/Unknown 86s 215ms/step - accuracy: 0.9343 - loss: 326.4187

399/Unknown 86s 215ms/step - accuracy: 0.9343 - loss: 326.2686

400/Unknown 87s 216ms/step - accuracy: 0.9343 - loss: 326.1197

401/Unknown 87s 216ms/step - accuracy: 0.9343 - loss: 325.9718

402/Unknown 87s 216ms/step - accuracy: 0.9344 - loss: 325.8240

403/Unknown 88s 216ms/step - accuracy: 0.9344 - loss: 325.6769

404/Unknown 88s 216ms/step - accuracy: 0.9344 - loss: 325.5306

405/Unknown 88s 216ms/step - accuracy: 0.9344 - loss: 325.3849

406/Unknown 88s 216ms/step - accuracy: 0.9345 - loss: 325.2393

407/Unknown 88s 216ms/step - accuracy: 0.9345 - loss: 325.0950

408/Unknown 89s 216ms/step - accuracy: 0.9345 - loss: 324.9518

409/Unknown 89s 216ms/step - accuracy: 0.9345 - loss: 324.8096

410/Unknown 89s 216ms/step - accuracy: 0.9346 - loss: 324.6682

411/Unknown 89s 216ms/step - accuracy: 0.9346 - loss: 324.5275

412/Unknown 90s 216ms/step - accuracy: 0.9346 - loss: 324.3878

413/Unknown 90s 216ms/step - accuracy: 0.9346 - loss: 324.2490

414/Unknown 90s 216ms/step - accuracy: 0.9347 - loss: 324.1107

415/Unknown 90s 216ms/step - accuracy: 0.9347 - loss: 323.9733

416/Unknown 90s 216ms/step - accuracy: 0.9347 - loss: 323.8363

417/Unknown 91s 216ms/step - accuracy: 0.9347 - loss: 323.7005

418/Unknown 91s 216ms/step - accuracy: 0.9347 - loss: 323.5650

419/Unknown 91s 216ms/step - accuracy: 0.9348 - loss: 323.4300

420/Unknown 91s 216ms/step - accuracy: 0.9348 - loss: 323.2954

421/Unknown 92s 216ms/step - accuracy: 0.9348 - loss: 323.1616

422/Unknown 92s 216ms/step - accuracy: 0.9348 - loss: 323.0279

423/Unknown 92s 216ms/step - accuracy: 0.9349 - loss: 322.8943

424/Unknown 92s 217ms/step - accuracy: 0.9349 - loss: 322.7610

425/Unknown 93s 217ms/step - accuracy: 0.9349 - loss: 322.6279

426/Unknown 93s 217ms/step - accuracy: 0.9349 - loss: 322.4956

427/Unknown 93s 217ms/step - accuracy: 0.9349 - loss: 322.3640

428/Unknown 93s 217ms/step - accuracy: 0.9350 - loss: 322.2333

429/Unknown 93s 217ms/step - accuracy: 0.9350 - loss: 322.1030

430/Unknown 94s 217ms/step - accuracy: 0.9350 - loss: 321.9735

431/Unknown 94s 217ms/step - accuracy: 0.9350 - loss: 321.8445

432/Unknown 94s 217ms/step - accuracy: 0.9350 - loss: 321.7156

433/Unknown 95s 217ms/step - accuracy: 0.9351 - loss: 321.5872

434/Unknown 95s 217ms/step - accuracy: 0.9351 - loss: 321.4588

435/Unknown 95s 217ms/step - accuracy: 0.9351 - loss: 321.3307

436/Unknown 95s 217ms/step - accuracy: 0.9351 - loss: 321.2033

437/Unknown 95s 217ms/step - accuracy: 0.9352 - loss: 321.0760

438/Unknown 96s 217ms/step - accuracy: 0.9352 - loss: 320.9493

439/Unknown 96s 217ms/step - accuracy: 0.9352 - loss: 320.8226

440/Unknown 96s 217ms/step - accuracy: 0.9352 - loss: 320.6959

441/Unknown 96s 217ms/step - accuracy: 0.9352 - loss: 320.5698

442/Unknown 97s 218ms/step - accuracy: 0.9353 - loss: 320.4443

443/Unknown 97s 218ms/step - accuracy: 0.9353 - loss: 320.3189

444/Unknown 97s 218ms/step - accuracy: 0.9353 - loss: 320.1938

445/Unknown 97s 218ms/step - accuracy: 0.9353 - loss: 320.0695

446/Unknown 98s 218ms/step - accuracy: 0.9353 - loss: 319.9457

447/Unknown 98s 218ms/step - accuracy: 0.9354 - loss: 319.8219

448/Unknown 98s 218ms/step - accuracy: 0.9354 - loss: 319.6988

449/Unknown 98s 218ms/step - accuracy: 0.9354 - loss: 319.5759

450/Unknown 99s 218ms/step - accuracy: 0.9354 - loss: 319.4533

451/Unknown 99s 218ms/step - accuracy: 0.9354 - loss: 319.3317

452/Unknown 99s 218ms/step - accuracy: 0.9355 - loss: 319.2104

453/Unknown 99s 218ms/step - accuracy: 0.9355 - loss: 319.0900

454/Unknown 100s 218ms/step - accuracy: 0.9355 - loss: 318.9702

455/Unknown 100s 218ms/step - accuracy: 0.9355 - loss: 318.8509

456/Unknown 100s 218ms/step - accuracy: 0.9355 - loss: 318.7319

457/Unknown 100s 218ms/step - accuracy: 0.9356 - loss: 318.6129

458/Unknown 101s 219ms/step - accuracy: 0.9356 - loss: 318.4945

459/Unknown 101s 219ms/step - accuracy: 0.9356 - loss: 318.3764

460/Unknown 101s 219ms/step - accuracy: 0.9356 - loss: 318.2592

461/Unknown 101s 219ms/step - accuracy: 0.9356 - loss: 318.1426

462/Unknown 102s 219ms/step - accuracy: 0.9356 - loss: 318.0262

463/Unknown 102s 219ms/step - accuracy: 0.9357 - loss: 317.9101

464/Unknown 102s 219ms/step - accuracy: 0.9357 - loss: 317.7942

465/Unknown 102s 219ms/step - accuracy: 0.9357 - loss: 317.6788

466/Unknown 102s 219ms/step - accuracy: 0.9357 - loss: 317.5635

467/Unknown 103s 219ms/step - accuracy: 0.9357 - loss: 317.4484

468/Unknown 103s 219ms/step - accuracy: 0.9358 - loss: 317.3335

469/Unknown 103s 219ms/step - accuracy: 0.9358 - loss: 317.2188

470/Unknown 103s 219ms/step - accuracy: 0.9358 - loss: 317.1040

471/Unknown 104s 219ms/step - accuracy: 0.9358 - loss: 316.9898

472/Unknown 104s 219ms/step - accuracy: 0.9358 - loss: 316.8763

473/Unknown 104s 219ms/step - accuracy: 0.9359 - loss: 316.7631

474/Unknown 104s 219ms/step - accuracy: 0.9359 - loss: 316.6501

475/Unknown 105s 219ms/step - accuracy: 0.9359 - loss: 316.5378

476/Unknown 105s 219ms/step - accuracy: 0.9359 - loss: 316.4257

477/Unknown 105s 220ms/step - accuracy: 0.9359 - loss: 316.3138

478/Unknown 105s 220ms/step - accuracy: 0.9359 - loss: 316.2022

479/Unknown 106s 220ms/step - accuracy: 0.9360 - loss: 316.0910

480/Unknown 106s 220ms/step - accuracy: 0.9360 - loss: 315.9806

481/Unknown 106s 220ms/step - accuracy: 0.9360 - loss: 315.8706

482/Unknown 106s 220ms/step - accuracy: 0.9360 - loss: 315.7607

483/Unknown 107s 220ms/step - accuracy: 0.9360 - loss: 315.6510

484/Unknown 107s 220ms/step - accuracy: 0.9361 - loss: 315.5415

485/Unknown 107s 220ms/step - accuracy: 0.9361 - loss: 315.4324

486/Unknown 107s 220ms/step - accuracy: 0.9361 - loss: 315.3241

487/Unknown 108s 220ms/step - accuracy: 0.9361 - loss: 315.2159

488/Unknown 108s 220ms/step - accuracy: 0.9361 - loss: 315.1081

489/Unknown 108s 220ms/step - accuracy: 0.9361 - loss: 315.0006

490/Unknown 108s 220ms/step - accuracy: 0.9362 - loss: 314.8934

491/Unknown 109s 220ms/step - accuracy: 0.9362 - loss: 314.7863

492/Unknown 109s 220ms/step - accuracy: 0.9362 - loss: 314.6796

493/Unknown 109s 220ms/step - accuracy: 0.9362 - loss: 314.5731

494/Unknown 109s 220ms/step - accuracy: 0.9362 - loss: 314.4671

495/Unknown 109s 220ms/step - accuracy: 0.9363 - loss: 314.3610

496/Unknown 110s 220ms/step - accuracy: 0.9363 - loss: 314.2551

497/Unknown 110s 220ms/step - accuracy: 0.9363 - loss: 314.1498

498/Unknown 110s 220ms/step - accuracy: 0.9363 - loss: 314.0451

499/Unknown 110s 220ms/step - accuracy: 0.9363 - loss: 313.9405

500/Unknown 111s 220ms/step - accuracy: 0.9363 - loss: 313.8364

501/Unknown 111s 220ms/step - accuracy: 0.9364 - loss: 313.7324

502/Unknown 111s 220ms/step - accuracy: 0.9364 - loss: 313.6290

503/Unknown 111s 220ms/step - accuracy: 0.9364 - loss: 313.5266

504/Unknown 111s 220ms/step - accuracy: 0.9364 - loss: 313.4240

505/Unknown 112s 220ms/step - accuracy: 0.9364 - loss: 313.3215

506/Unknown 112s 220ms/step - accuracy: 0.9364 - loss: 313.2194

507/Unknown 112s 220ms/step - accuracy: 0.9365 - loss: 313.1174

508/Unknown 112s 220ms/step - accuracy: 0.9365 - loss: 313.0163

509/Unknown 113s 220ms/step - accuracy: 0.9365 - loss: 312.9149

510/Unknown 113s 220ms/step - accuracy: 0.9365 - loss: 312.8137

511/Unknown 113s 220ms/step - accuracy: 0.9365 - loss: 312.7127

512/Unknown 113s 220ms/step - accuracy: 0.9365 - loss: 312.6117

513/Unknown 113s 220ms/step - accuracy: 0.9366 - loss: 312.5108

514/Unknown 114s 220ms/step - accuracy: 0.9366 - loss: 312.4105

515/Unknown 114s 220ms/step - accuracy: 0.9366 - loss: 312.3107

516/Unknown 114s 221ms/step - accuracy: 0.9366 - loss: 312.2112

517/Unknown 115s 221ms/step - accuracy: 0.9366 - loss: 312.1120

518/Unknown 115s 221ms/step - accuracy: 0.9366 - loss: 312.0136

519/Unknown 115s 221ms/step - accuracy: 0.9367 - loss: 311.9154

520/Unknown 115s 221ms/step - accuracy: 0.9367 - loss: 311.8175

521/Unknown 116s 221ms/step - accuracy: 0.9367 - loss: 311.7198

522/Unknown 116s 221ms/step - accuracy: 0.9367 - loss: 311.6223

523/Unknown 116s 221ms/step - accuracy: 0.9367 - loss: 311.5254

524/Unknown 116s 221ms/step - accuracy: 0.9367 - loss: 311.4289

525/Unknown 117s 221ms/step - accuracy: 0.9368 - loss: 311.3327

526/Unknown 117s 221ms/step - accuracy: 0.9368 - loss: 311.2370

527/Unknown 117s 221ms/step - accuracy: 0.9368 - loss: 311.1415

528/Unknown 117s 221ms/step - accuracy: 0.9368 - loss: 311.0460

529/Unknown 118s 221ms/step - accuracy: 0.9368 - loss: 310.9506

530/Unknown 118s 221ms/step - accuracy: 0.9368 - loss: 310.8556

531/Unknown 118s 221ms/step - accuracy: 0.9368 - loss: 310.7611

532/Unknown 118s 221ms/step - accuracy: 0.9369 - loss: 310.6666

533/Unknown 119s 222ms/step - accuracy: 0.9369 - loss: 310.5723

534/Unknown 119s 222ms/step - accuracy: 0.9369 - loss: 310.4783

535/Unknown 119s 222ms/step - accuracy: 0.9369 - loss: 310.3845

536/Unknown 119s 222ms/step - accuracy: 0.9369 - loss: 310.2907

537/Unknown 119s 222ms/step - accuracy: 0.9369 - loss: 310.1972

538/Unknown 120s 222ms/step - accuracy: 0.9370 - loss: 310.1041

539/Unknown 120s 222ms/step - accuracy: 0.9370 - loss: 310.0114

540/Unknown 120s 222ms/step - accuracy: 0.9370 - loss: 309.9187

541/Unknown 120s 222ms/step - accuracy: 0.9370 - loss: 309.8263

542/Unknown 121s 221ms/step - accuracy: 0.9370 - loss: 309.7338

543/Unknown 121s 221ms/step - accuracy: 0.9370 - loss: 309.6414

544/Unknown 121s 222ms/step - accuracy: 0.9370 - loss: 309.5492

545/Unknown 121s 222ms/step - accuracy: 0.9371 - loss: 309.4574

546/Unknown 121s 222ms/step - accuracy: 0.9371 - loss: 309.3658

547/Unknown 122s 222ms/step - accuracy: 0.9371 - loss: 309.2743

548/Unknown 122s 222ms/step - accuracy: 0.9371 - loss: 309.1831

549/Unknown 122s 222ms/step - accuracy: 0.9371 - loss: 309.0924

550/Unknown 122s 222ms/step - accuracy: 0.9371 - loss: 309.0020

551/Unknown 123s 222ms/step - accuracy: 0.9371 - loss: 308.9121

552/Unknown 123s 222ms/step - accuracy: 0.9372 - loss: 308.8221

553/Unknown 123s 222ms/step - accuracy: 0.9372 - loss: 308.7321

554/Unknown 123s 222ms/step - accuracy: 0.9372 - loss: 308.6426

555/Unknown 124s 222ms/step - accuracy: 0.9372 - loss: 308.5534

556/Unknown 124s 222ms/step - accuracy: 0.9372 - loss: 308.4645

557/Unknown 124s 222ms/step - accuracy: 0.9372 - loss: 308.3757

558/Unknown 124s 222ms/step - accuracy: 0.9372 - loss: 308.2873

559/Unknown 125s 222ms/step - accuracy: 0.9373 - loss: 308.1992

560/Unknown 125s 222ms/step - accuracy: 0.9373 - loss: 308.1115

561/Unknown 125s 222ms/step - accuracy: 0.9373 - loss: 308.0239

562/Unknown 125s 222ms/step - accuracy: 0.9373 - loss: 307.9366

563/Unknown 126s 222ms/step - accuracy: 0.9373 - loss: 307.8493

564/Unknown 126s 222ms/step - accuracy: 0.9373 - loss: 307.7624

565/Unknown 126s 222ms/step - accuracy: 0.9373 - loss: 307.6758

566/Unknown 126s 222ms/step - accuracy: 0.9374 - loss: 307.5896

567/Unknown 126s 222ms/step - accuracy: 0.9374 - loss: 307.5038

568/Unknown 127s 222ms/step - accuracy: 0.9374 - loss: 307.4184

569/Unknown 127s 222ms/step - accuracy: 0.9374 - loss: 307.3336

570/Unknown 127s 222ms/step - accuracy: 0.9374 - loss: 307.2491

571/Unknown 127s 222ms/step - accuracy: 0.9374 - loss: 307.1649

572/Unknown 128s 222ms/step - accuracy: 0.9374 - loss: 307.0807

573/Unknown 128s 222ms/step - accuracy: 0.9375 - loss: 306.9967

574/Unknown 128s 223ms/step - accuracy: 0.9375 - loss: 306.9132

575/Unknown 129s 223ms/step - accuracy: 0.9375 - loss: 306.8299

576/Unknown 129s 223ms/step - accuracy: 0.9375 - loss: 306.7468

577/Unknown 129s 223ms/step - accuracy: 0.9375 - loss: 306.6639

578/Unknown 129s 223ms/step - accuracy: 0.9375 - loss: 306.5809

579/Unknown 130s 223ms/step - accuracy: 0.9375 - loss: 306.4984

580/Unknown 130s 223ms/step - accuracy: 0.9376 - loss: 306.4160

581/Unknown 130s 223ms/step - accuracy: 0.9376 - loss: 306.3336

582/Unknown 130s 223ms/step - accuracy: 0.9376 - loss: 306.2515

583/Unknown 131s 223ms/step - accuracy: 0.9376 - loss: 306.1693

584/Unknown 131s 223ms/step - accuracy: 0.9376 - loss: 306.0871

585/Unknown 131s 223ms/step - accuracy: 0.9376 - loss: 306.0051

586/Unknown 131s 223ms/step - accuracy: 0.9376 - loss: 305.9232

587/Unknown 132s 223ms/step - accuracy: 0.9376 - loss: 305.8415

588/Unknown 132s 223ms/step - accuracy: 0.9377 - loss: 305.7600

589/Unknown 132s 223ms/step - accuracy: 0.9377 - loss: 305.6787

590/Unknown 132s 223ms/step - accuracy: 0.9377 - loss: 305.5978

591/Unknown 133s 223ms/step - accuracy: 0.9377 - loss: 305.5169

592/Unknown 133s 223ms/step - accuracy: 0.9377 - loss: 305.4363

593/Unknown 133s 223ms/step - accuracy: 0.9377 - loss: 305.3560

594/Unknown 133s 223ms/step - accuracy: 0.9377 - loss: 305.2758

595/Unknown 133s 223ms/step - accuracy: 0.9378 - loss: 305.1960

596/Unknown 134s 223ms/step - accuracy: 0.9378 - loss: 305.1167

597/Unknown 134s 223ms/step - accuracy: 0.9378 - loss: 305.0374

598/Unknown 134s 223ms/step - accuracy: 0.9378 - loss: 304.9583

599/Unknown 134s 223ms/step - accuracy: 0.9378 - loss: 304.8791

600/Unknown 134s 223ms/step - accuracy: 0.9378 - loss: 304.8002

601/Unknown 135s 223ms/step - accuracy: 0.9378 - loss: 304.7217

602/Unknown 135s 223ms/step - accuracy: 0.9378 - loss: 304.6434

603/Unknown 135s 223ms/step - accuracy: 0.9379 - loss: 304.5656

604/Unknown 135s 223ms/step - accuracy: 0.9379 - loss: 304.4879

605/Unknown 136s 223ms/step - accuracy: 0.9379 - loss: 304.4105

606/Unknown 136s 223ms/step - accuracy: 0.9379 - loss: 304.3331

607/Unknown 136s 223ms/step - accuracy: 0.9379 - loss: 304.2561

608/Unknown 136s 223ms/step - accuracy: 0.9379 - loss: 304.1792

609/Unknown 137s 223ms/step - accuracy: 0.9379 - loss: 304.1025

610/Unknown 137s 223ms/step - accuracy: 0.9379 - loss: 304.0259

611/Unknown 137s 223ms/step - accuracy: 0.9380 - loss: 303.9494

612/Unknown 137s 223ms/step - accuracy: 0.9380 - loss: 303.8730

613/Unknown 137s 223ms/step - accuracy: 0.9380 - loss: 303.7966

614/Unknown 138s 223ms/step - accuracy: 0.9380 - loss: 303.7203

615/Unknown 138s 223ms/step - accuracy: 0.9380 - loss: 303.6440

616/Unknown 138s 223ms/step - accuracy: 0.9380 - loss: 303.5678

617/Unknown 138s 224ms/step - accuracy: 0.9380 - loss: 303.4917

618/Unknown 139s 224ms/step - accuracy: 0.9380 - loss: 303.4161

619/Unknown 139s 224ms/step - accuracy: 0.9380 - loss: 303.3405

620/Unknown 139s 224ms/step - accuracy: 0.9381 - loss: 303.2652

621/Unknown 140s 224ms/step - accuracy: 0.9381 - loss: 303.1904

622/Unknown 140s 224ms/step - accuracy: 0.9381 - loss: 303.1159

623/Unknown 140s 224ms/step - accuracy: 0.9381 - loss: 303.0418

624/Unknown 140s 224ms/step - accuracy: 0.9381 - loss: 302.9678

625/Unknown 141s 224ms/step - accuracy: 0.9381 - loss: 302.8940

626/Unknown 141s 224ms/step - accuracy: 0.9381 - loss: 302.8201

627/Unknown 141s 224ms/step - accuracy: 0.9381 - loss: 302.7465

628/Unknown 141s 224ms/step - accuracy: 0.9382 - loss: 302.6730

629/Unknown 141s 224ms/step - accuracy: 0.9382 - loss: 302.5998

630/Unknown 142s 224ms/step - accuracy: 0.9382 - loss: 302.5268

631/Unknown 142s 224ms/step - accuracy: 0.9382 - loss: 302.4539

632/Unknown 142s 224ms/step - accuracy: 0.9382 - loss: 302.3810

633/Unknown 142s 224ms/step - accuracy: 0.9382 - loss: 302.3086

634/Unknown 143s 224ms/step - accuracy: 0.9382 - loss: 302.2364

635/Unknown 143s 224ms/step - accuracy: 0.9382 - loss: 302.1645

636/Unknown 143s 224ms/step - accuracy: 0.9383 - loss: 302.0930

637/Unknown 143s 224ms/step - accuracy: 0.9383 - loss: 302.0216

638/Unknown 144s 224ms/step - accuracy: 0.9383 - loss: 301.9502

639/Unknown 144s 224ms/step - accuracy: 0.9383 - loss: 301.8791

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches. You may need to use the .repeat() function when building your dataset. self._interrupted_warning()

</div>
 639/639 ━━━━━━━━━━━━━━━━━━━━ 160s 249ms/step - accuracy: 0.9383 - loss: 301.8082 - val_accuracy: 0.9485 - val_loss: 235.7996


<div class="k-default-codeblock">

Model training finished. Evaluating model performance...

</div>
    
<div class="k-default-codeblock">

1/Unknown 0s 331ms/step - accuracy: 0.9623 - loss: 160.6135

2/Unknown 0s 119ms/step - accuracy: 0.9557 - loss: 181.4366

3/Unknown 1s 131ms/step - accuracy: 0.9524 - loss: 198.4659

4/Unknown 1s 129ms/step - accuracy: 0.9502 - loss: 209.3009

5/Unknown 1s 133ms/step - accuracy: 0.9499 - loss: 215.6982

6/Unknown 1s 131ms/step - accuracy: 0.9499 - loss: 219.7466

7/Unknown 1s 132ms/step - accuracy: 0.9502 - loss: 220.2296

8/Unknown 1s 132ms/step - accuracy: 0.9504 - loss: 219.6000

9/Unknown 1s 133ms/step - accuracy: 0.9506 - loss: 218.5403

10/Unknown 2s 133ms/step - accuracy: 0.9507 - loss: 217.4007

11/Unknown 2s 134ms/step - accuracy: 0.9507 - loss: 216.4865

12/Unknown 2s 133ms/step - accuracy: 0.9504 - loss: 215.7090

13/Unknown 2s 135ms/step - accuracy: 0.9502 - loss: 215.4628

14/Unknown 2s 135ms/step - accuracy: 0.9500 - loss: 215.0735

15/Unknown 2s 134ms/step - accuracy: 0.9499 - loss: 214.8078

16/Unknown 2s 134ms/step - accuracy: 0.9500 - loss: 214.3558

17/Unknown 2s 134ms/step - accuracy: 0.9500 - loss: 213.9521

18/Unknown 3s 135ms/step - accuracy: 0.9501 - loss: 213.9012

19/Unknown 3s 134ms/step - accuracy: 0.9501 - loss: 214.0063

20/Unknown 3s 135ms/step - accuracy: 0.9501 - loss: 214.2168

21/Unknown 3s 134ms/step - accuracy: 0.9500 - loss: 214.5657

22/Unknown 3s 135ms/step - accuracy: 0.9500 - loss: 214.8618

23/Unknown 3s 134ms/step - accuracy: 0.9500 - loss: 215.1154

24/Unknown 3s 135ms/step - accuracy: 0.9499 - loss: 215.2906

25/Unknown 4s 134ms/step - accuracy: 0.9499 - loss: 215.6145

26/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 215.8544

27/Unknown 4s 135ms/step - accuracy: 0.9498 - loss: 216.0591

28/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.2666

29/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.4423

30/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.5613

31/Unknown 4s 135ms/step - accuracy: 0.9498 - loss: 216.7220

32/Unknown 5s 135ms/step - accuracy: 0.9498 - loss: 216.8842

33/Unknown 5s 135ms/step - accuracy: 0.9498 - loss: 217.1658

34/Unknown 5s 135ms/step - accuracy: 0.9497 - loss: 217.4608

35/Unknown 5s 135ms/step - accuracy: 0.9496 - loss: 217.7231

36/Unknown 5s 134ms/step - accuracy: 0.9496 - loss: 217.9504

37/Unknown 5s 135ms/step - accuracy: 0.9496 - loss: 218.1658

38/Unknown 5s 134ms/step - accuracy: 0.9495 - loss: 218.3597

39/Unknown 5s 134ms/step - accuracy: 0.9495 - loss: 218.5269

40/Unknown 6s 135ms/step - accuracy: 0.9495 - loss: 218.7106

41/Unknown 6s 135ms/step - accuracy: 0.9494 - loss: 218.8721

42/Unknown 6s 136ms/step - accuracy: 0.9494 - loss: 218.9924

43/Unknown 6s 136ms/step - accuracy: 0.9494 - loss: 219.1745

44/Unknown 6s 137ms/step - accuracy: 0.9494 - loss: 219.3449

45/Unknown 6s 138ms/step - accuracy: 0.9493 - loss: 219.4943

46/Unknown 7s 137ms/step - accuracy: 0.9493 - loss: 219.6201

47/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.7240

48/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.8335

49/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.9367

50/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 220.0834

51/Unknown 7s 138ms/step - accuracy: 0.9492 - loss: 220.2067

52/Unknown 7s 139ms/step - accuracy: 0.9492 - loss: 220.2963

53/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.3649

54/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.4462

55/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.5459

56/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.6197

57/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.6727

58/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.7652

59/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.8513

60/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.9392

61/Unknown 9s 138ms/step - accuracy: 0.9491 - loss: 221.0330

62/Unknown 9s 137ms/step - accuracy: 0.9491 - loss: 221.1127

63/Unknown 9s 137ms/step - accuracy: 0.9491 - loss: 221.2177

64/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.3382

65/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.4572

66/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.5552

67/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.6626

68/Unknown 9s 136ms/step - accuracy: 0.9490 - loss: 221.7653

69/Unknown 10s 136ms/step - accuracy: 0.9490 - loss: 221.8680

70/Unknown 10s 136ms/step - accuracy: 0.9490 - loss: 221.9582

71/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.0398

72/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.1409

73/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.2496

74/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.3526

75/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.4433

76/Unknown 11s 136ms/step - accuracy: 0.9489 - loss: 222.5272

77/Unknown 11s 136ms/step - accuracy: 0.9489 - loss: 222.6031

78/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.6857

79/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.7623

80/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.8322

81/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.8963

82/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.9694

83/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 223.0455

84/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.1209

85/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.1990

86/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.2825

87/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.3633

88/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.4366

89/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.5151

90/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.5958

91/Unknown 13s 136ms/step - accuracy: 0.9487 - loss: 223.6727

92/Unknown 13s 137ms/step - accuracy: 0.9487 - loss: 223.7505

93/Unknown 13s 137ms/step - accuracy: 0.9487 - loss: 223.8250

94/Unknown 13s 137ms/step - accuracy: 0.9486 - loss: 223.9114

95/Unknown 13s 137ms/step - accuracy: 0.9486 - loss: 223.9948

96/Unknown 13s 138ms/step - accuracy: 0.9486 - loss: 224.0807

97/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.1586

98/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.2289

99/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.2979

100/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.3739

101/Unknown 14s 138ms/step - accuracy: 0.9485 - loss: 224.4488

102/Unknown 14s 138ms/step - accuracy: 0.9485 - loss: 224.5210

103/Unknown 14s 139ms/step - accuracy: 0.9485 - loss: 224.5936

104/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.6630

105/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.7316

106/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.8002

107/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.8736

108/Unknown 15s 139ms/step - accuracy: 0.9484 - loss: 224.9466

109/Unknown 15s 138ms/step - accuracy: 0.9484 - loss: 225.0268

110/Unknown 15s 138ms/step - accuracy: 0.9484 - loss: 225.1065

111/Unknown 16s 138ms/step - accuracy: 0.9484 - loss: 225.1895

112/Unknown 16s 139ms/step - accuracy: 0.9484 - loss: 225.2730

113/Unknown 16s 139ms/step - accuracy: 0.9484 - loss: 225.3562

114/Unknown 16s 139ms/step - accuracy: 0.9484 - loss: 225.4317

115/Unknown 16s 139ms/step - accuracy: 0.9484 - loss: 225.5018

116/Unknown 16s 139ms/step - accuracy: 0.9483 - loss: 225.5749

117/Unknown 16s 139ms/step - accuracy: 0.9483 - loss: 225.6508

118/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 225.7233

119/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 225.7933

120/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 225.8627

121/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 225.9334

122/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 226.0017

123/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 226.0612

124/Unknown 17s 139ms/step - accuracy: 0.9483 - loss: 226.1206

125/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.1817

126/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.2397

127/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.2974

128/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.3512

129/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.4005

130/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.4489

131/Unknown 18s 139ms/step - accuracy: 0.9482 - loss: 226.4945

132/Unknown 19s 139ms/step - accuracy: 0.9482 - loss: 226.5418

133/Unknown 19s 139ms/step - accuracy: 0.9482 - loss: 226.5853

134/Unknown 19s 140ms/step - accuracy: 0.9482 - loss: 226.6308

135/Unknown 19s 140ms/step - accuracy: 0.9482 - loss: 226.6727

136/Unknown 19s 140ms/step - accuracy: 0.9482 - loss: 226.7198

137/Unknown 19s 140ms/step - accuracy: 0.9482 - loss: 226.7688

138/Unknown 19s 140ms/step - accuracy: 0.9482 - loss: 226.8152

139/Unknown 20s 140ms/step - accuracy: 0.9482 - loss: 226.8609

140/Unknown 20s 140ms/step - accuracy: 0.9482 - loss: 226.9056

141/Unknown 20s 140ms/step - accuracy: 0.9481 - loss: 226.9498

142/Unknown 20s 140ms/step - accuracy: 0.9481 - loss: 226.9936

143/Unknown 20s 140ms/step - accuracy: 0.9481 - loss: 227.0366

144/Unknown 20s 139ms/step - accuracy: 0.9481 - loss: 227.0785

145/Unknown 20s 139ms/step - accuracy: 0.9481 - loss: 227.1189

146/Unknown 21s 139ms/step - accuracy: 0.9481 - loss: 227.1589

147/Unknown 21s 139ms/step - accuracy: 0.9481 - loss: 227.1976

148/Unknown 21s 140ms/step - accuracy: 0.9481 - loss: 227.2341

149/Unknown 21s 140ms/step - accuracy: 0.9481 - loss: 227.2737

150/Unknown 21s 140ms/step - accuracy: 0.9481 - loss: 227.3114

151/Unknown 21s 140ms/step - accuracy: 0.9481 - loss: 227.3518

152/Unknown 21s 140ms/step - accuracy: 0.9481 - loss: 227.3940

153/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.4359

154/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.4770

155/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.5203

156/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.5634

157/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.6052

158/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.6457

159/Unknown 22s 140ms/step - accuracy: 0.9481 - loss: 227.6858

160/Unknown 23s 140ms/step - accuracy: 0.9481 - loss: 227.7236

161/Unknown 23s 140ms/step - accuracy: 0.9481 - loss: 227.7598

162/Unknown 23s 140ms/step - accuracy: 0.9481 - loss: 227.7944

163/Unknown 23s 140ms/step - accuracy: 0.9481 - loss: 227.8277

164/Unknown 23s 140ms/step - accuracy: 0.9481 - loss: 227.8596

165/Unknown 23s 140ms/step - accuracy: 0.9480 - loss: 227.8896

166/Unknown 23s 140ms/step - accuracy: 0.9480 - loss: 227.9187

167/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 227.9463

168/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 227.9726

169/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 227.9972

170/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 228.0219

171/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 228.0469

172/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 228.0727

173/Unknown 24s 140ms/step - accuracy: 0.9480 - loss: 228.0992

174/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.1232

175/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.1466

176/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.1700

177/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.1928

178/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.2146

179/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.2334

180/Unknown 25s 140ms/step - accuracy: 0.9480 - loss: 228.2502

181/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.2660

182/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.2829

183/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3001

184/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3197

185/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3382

186/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3550

187/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3710

188/Unknown 26s 140ms/step - accuracy: 0.9480 - loss: 228.3866

189/Unknown 27s 140ms/step - accuracy: 0.9480 - loss: 228.4023

190/Unknown 27s 140ms/step - accuracy: 0.9480 - loss: 228.4187

191/Unknown 27s 140ms/step - accuracy: 0.9480 - loss: 228.4339

192/Unknown 27s 139ms/step - accuracy: 0.9480 - loss: 228.4476

193/Unknown 27s 139ms/step - accuracy: 0.9480 - loss: 228.4600

194/Unknown 27s 139ms/step - accuracy: 0.9480 - loss: 228.4714

195/Unknown 27s 139ms/step - accuracy: 0.9480 - loss: 228.4833

196/Unknown 27s 139ms/step - accuracy: 0.9480 - loss: 228.4972

197/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5084

198/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5233

199/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5378

200/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5527

201/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5660

202/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5789

203/Unknown 28s 139ms/step - accuracy: 0.9480 - loss: 228.5915

204/Unknown 29s 139ms/step - accuracy: 0.9480 - loss: 228.6026

205/Unknown 29s 139ms/step - accuracy: 0.9480 - loss: 228.6157

206/Unknown 29s 139ms/step - accuracy: 0.9480 - loss: 228.6292

207/Unknown 29s 139ms/step - accuracy: 0.9481 - loss: 228.6418

208/Unknown 29s 139ms/step - accuracy: 0.9481 - loss: 228.6543

209/Unknown 29s 139ms/step - accuracy: 0.9481 - loss: 228.6688

210/Unknown 29s 139ms/step - accuracy: 0.9481 - loss: 228.6819

211/Unknown 29s 139ms/step - accuracy: 0.9481 - loss: 228.6941

212/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7058

213/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7174

214/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7294

215/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7410

216/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7537

217/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7657

218/Unknown 30s 139ms/step - accuracy: 0.9481 - loss: 228.7765

219/Unknown 31s 139ms/step - accuracy: 0.9481 - loss: 228.7861

220/Unknown 31s 139ms/step - accuracy: 0.9481 - loss: 228.7945

221/Unknown 31s 139ms/step - accuracy: 0.9481 - loss: 228.8020

222/Unknown 31s 139ms/step - accuracy: 0.9481 - loss: 228.8082

223/Unknown 31s 139ms/step - accuracy: 0.9481 - loss: 228.8140

224/Unknown 31s 138ms/step - accuracy: 0.9481 - loss: 228.8199

225/Unknown 31s 138ms/step - accuracy: 0.9481 - loss: 228.8271

226/Unknown 31s 138ms/step - accuracy: 0.9481 - loss: 228.8340

227/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8403

228/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8456

229/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8512

230/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8562

231/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8611

232/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8642

233/Unknown 32s 138ms/step - accuracy: 0.9481 - loss: 228.8672

234/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8694

235/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8721

236/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8762

237/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8797

238/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8838

239/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8885

240/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8919

241/Unknown 33s 138ms/step - accuracy: 0.9481 - loss: 228.8947

242/Unknown 34s 138ms/step - accuracy: 0.9481 - loss: 228.8974

243/Unknown 34s 138ms/step - accuracy: 0.9481 - loss: 228.9001

244/Unknown 34s 138ms/step - accuracy: 0.9481 - loss: 228.9023

245/Unknown 34s 138ms/step - accuracy: 0.9481 - loss: 228.9047

246/Unknown 34s 138ms/step - accuracy: 0.9481 - loss: 228.9063

247/Unknown 34s 139ms/step - accuracy: 0.9481 - loss: 228.9082

248/Unknown 35s 139ms/step - accuracy: 0.9481 - loss: 228.9095

249/Unknown 35s 139ms/step - accuracy: 0.9482 - loss: 228.9103

250/Unknown 35s 139ms/step - accuracy: 0.9482 - loss: 228.9118

251/Unknown 35s 139ms/step - accuracy: 0.9482 - loss: 228.9147

252/Unknown 35s 139ms/step - accuracy: 0.9482 - loss: 228.9173

253/Unknown 35s 139ms/step - accuracy: 0.9482 - loss: 228.9200

254/Unknown 36s 139ms/step - accuracy: 0.9482 - loss: 228.9222

255/Unknown 36s 139ms/step - accuracy: 0.9482 - loss: 228.9243

256/Unknown 36s 139ms/step - accuracy: 0.9482 - loss: 228.9268

257/Unknown 36s 139ms/step - accuracy: 0.9482 - loss: 228.9288

258/Unknown 36s 140ms/step - accuracy: 0.9482 - loss: 228.9315

259/Unknown 36s 140ms/step - accuracy: 0.9482 - loss: 228.9344

260/Unknown 36s 140ms/step - accuracy: 0.9482 - loss: 228.9363

261/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9379

262/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9381

263/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9378

264/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9372

265/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9366

266/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9373

267/Unknown 37s 140ms/step - accuracy: 0.9482 - loss: 228.9385

268/Unknown 38s 140ms/step - accuracy: 0.9482 - loss: 228.9391

269/Unknown 38s 140ms/step - accuracy: 0.9482 - loss: 228.9401

270/Unknown 38s 140ms/step - accuracy: 0.9482 - loss: 228.9409

271/Unknown 38s 139ms/step - accuracy: 0.9482 - loss: 228.9415

272/Unknown 38s 139ms/step - accuracy: 0.9482 - loss: 228.9413

273/Unknown 38s 139ms/step - accuracy: 0.9482 - loss: 228.9413

274/Unknown 38s 139ms/step - accuracy: 0.9482 - loss: 228.9406

275/Unknown 38s 139ms/step - accuracy: 0.9482 - loss: 228.9396

276/Unknown 39s 139ms/step - accuracy: 0.9482 - loss: 228.9395

277/Unknown 39s 139ms/step - accuracy: 0.9482 - loss: 228.9393

278/Unknown 39s 139ms/step - accuracy: 0.9482 - loss: 228.9390

279/Unknown 39s 139ms/step - accuracy: 0.9482 - loss: 228.9383

280/Unknown 39s 139ms/step - accuracy: 0.9483 - loss: 228.9368

281/Unknown 39s 139ms/step - accuracy: 0.9483 - loss: 228.9358

282/Unknown 39s 139ms/step - accuracy: 0.9483 - loss: 228.9339

283/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9312

284/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9295

285/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9271

286/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9249

287/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9228

288/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9200

289/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9166

290/Unknown 40s 139ms/step - accuracy: 0.9483 - loss: 228.9136

291/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.9105

292/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.9076

293/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.9045

294/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.9017

295/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.8983

296/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.8967

297/Unknown 41s 139ms/step - accuracy: 0.9483 - loss: 228.8956

298/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8946

299/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8931

300/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8911

301/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8904

302/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8897

303/Unknown 42s 139ms/step - accuracy: 0.9483 - loss: 228.8902

304/Unknown 42s 139ms/step - accuracy: 0.9484 - loss: 228.8911

305/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8920

306/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8923

307/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8938

308/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8946

309/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8958

310/Unknown 43s 139ms/step - accuracy: 0.9484 - loss: 228.8979

311/Unknown 44s 139ms/step - accuracy: 0.9484 - loss: 228.9006

312/Unknown 44s 139ms/step - accuracy: 0.9484 - loss: 228.9027

313/Unknown 44s 139ms/step - accuracy: 0.9484 - loss: 228.9051

314/Unknown 44s 139ms/step - accuracy: 0.9484 - loss: 228.9074

315/Unknown 44s 140ms/step - accuracy: 0.9484 - loss: 228.9098

316/Unknown 44s 140ms/step - accuracy: 0.9484 - loss: 228.9117

317/Unknown 44s 140ms/step - accuracy: 0.9484 - loss: 228.9142

318/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9171

319/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9203

320/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9232

321/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9262

322/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9293

323/Unknown 45s 140ms/step - accuracy: 0.9484 - loss: 228.9324

324/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9353

325/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9387

326/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9429

327/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9469

328/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9508

329/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9543

330/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9582

331/Unknown 46s 140ms/step - accuracy: 0.9484 - loss: 228.9617

332/Unknown 47s 140ms/step - accuracy: 0.9484 - loss: 228.9662

333/Unknown 47s 140ms/step - accuracy: 0.9484 - loss: 228.9707

334/Unknown 47s 140ms/step - accuracy: 0.9484 - loss: 228.9747

335/Unknown 47s 140ms/step - accuracy: 0.9484 - loss: 228.9783

336/Unknown 47s 140ms/step - accuracy: 0.9485 - loss: 228.9814

337/Unknown 47s 140ms/step - accuracy: 0.9485 - loss: 228.9847

338/Unknown 47s 140ms/step - accuracy: 0.9485 - loss: 228.9882

339/Unknown 47s 140ms/step - accuracy: 0.9485 - loss: 228.9913

340/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 228.9950

341/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 228.9996

342/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 229.0038

343/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 229.0080

344/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 229.0122

345/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 229.0163

346/Unknown 48s 139ms/step - accuracy: 0.9485 - loss: 229.0210

347/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0256

348/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0313

349/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0372

350/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0429

351/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0492

352/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0551

353/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0615

354/Unknown 49s 139ms/step - accuracy: 0.9485 - loss: 229.0690

355/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.0767

356/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.0841

357/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.0911

358/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.0985

359/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.1061

360/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.1136

361/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.1216

362/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1297

363/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1375

364/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1453

365/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1526

366/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1591

367/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1653

368/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1717

369/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1778

370/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1837

371/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1893

372/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1947

373/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1999

374/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2054

375/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2110

376/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2163

377/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2217

</div>
 377/377 ━━━━━━━━━━━━━━━━━━━━ 53s 139ms/step - accuracy: 0.9486 - loss: 229.2270


<div class="k-default-codeblock">

Test accuracy: 94.94%

</div>
You should achieve more than 95% accuracy on the test set.

To increase the learning capacity of the model, you can try increasing the
`encoding_size` value, or stacking multiple GRN layers on top of the VSN layer.
This may require to also increase the `dropout_rate` value to avoid overfitting.

**Example available on HuggingFace**

| Trained Model | Demo |
| :--: | :--: |
| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Classification%20With%20GRN%20%26%20VSN-red)](https://huggingface.co/keras-io/structured-data-classification-grn-vsn) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Space-Classification%20With%20GRN%20%26%20VSN-red)](https://huggingface.co/spaces/keras-io/structured-data-classification-grn-vsn) |

Classification with Gated Residual and Variable Selection Networks

Introduction

The dataset

Setup

Prepare the data

Define dataset metadata

Create a `tf.data.Dataset` for training and evaluation

Create model inputs

Implement the Gated Linear Unit

Implement the Gated Residual Network

Implement the Variable Selection Network

Create Gated Residual and Variable Selection Networks model

Compile, train, and evaluate the model

Product

Resources

Company

Classification with Gated Residual and Variable Selection Networks

Introduction

The dataset

Setup

Prepare the data

Define dataset metadata

Create a tf.data.Dataset for training and evaluation

Create model inputs

Implement the Gated Linear Unit

Implement the Gated Residual Network

Implement the Variable Selection Network

Create Gated Residual and Variable Selection Networks model

Compile, train, and evaluate the model

Create a `tf.data.Dataset` for training and evaluation