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class Sampling(tf.keras.layers.Layer): | |
def call(self, args): | |
z_mean, z_log_var = args | |
batch = tf.shape(z_mean)[0] | |
dim = tf.shape(z_mean)[1] | |
epsilon = tf.random.normal(shape=(batch, dim), mean=0., stddev=1.) | |
return z_mean + epsilon * tf.exp(0.5 * z_log_var) |
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class Decoder(tf.keras.layers.Layer): | |
def __init__(self, original_dim): | |
super(Decoder, self).__init__() | |
self.hidden_layer_1 = tf.keras.layers.Dense(units=32, activation=tf.nn.relu) | |
self.hidden_layer_2 = tf.keras.layers.Dense(units=64, activation=tf.nn.relu) | |
self.hidden_layer_3 = tf.keras.layers.Dense(units=128, activation=tf.nn.relu) | |
self.output_layer = tf.keras.layers.Dense(units=original_dim, activation=tf.nn.sigmoid) | |
def call(self, input_features): | |
activation_1 = self.hidden_layer_1(input_features) |
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class Encoder(tf.keras.layers.Layer): | |
def __init__(self, latent_dim): | |
super(Encoder, self).__init__() | |
self.input_layer = tf.keras.layers.InputLayer(input_shape=(28, 28, 1)) | |
self.reshape = tf.keras.layers.Reshape(target_shape=(784, )) | |
self.hidden_layer_1 = tf.keras.layers.Dense(units=128, activation=tf.nn.relu) | |
self.hidden_layer_2 = tf.keras.layers.Dense(units=64, activation=tf.nn.relu) | |
self.hidden_layer_3 = tf.keras.layers.Dense(units=32, activation=tf.nn.relu) | |
self.z_mean_layer = tf.keras.layers.Dense(units=latent_dim) | |
self.z_log_var_layer = tf.keras.layers.Dense(units=latent_dim) |
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autoencoder = Autoencoder(intermediate_dim=64, original_dim=784) | |
opt = tf.optimizers.Adam(learning_rate=learning_rate) | |
(training_features, _), (test_features, _) = tf.keras.datasets.mnist.load_data() | |
training_features = training_features / np.max(training_features) | |
training_features = training_features.reshape(training_features.shape[0], | |
training_features.shape[1] * training_features.shape[2]) | |
training_features = training_features.astype('float32') | |
training_dataset = tf.data.Dataset.from_tensor_slices(training_features) | |
training_dataset = training_dataset.batch(batch_size) |
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def train(loss, model, opt, original): | |
with tf.GradientTape() as tape: | |
gradients = tape.gradient(loss(model, original), model.trainable_variables) | |
gradient_variables = zip(gradients, model.trainable_variables) | |
opt.apply_gradients(gradient_variables) |
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def loss(model, original): | |
reconstruction_error = tf.reduce_mean(tf.square(tf.subtract(model(original), original))) | |
return reconstruction_error |
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class Autoencoder(tf.keras.Model): | |
def __init__(self, intermediate_dim, original_dim): | |
super(Autoencoder, self).__init__() | |
self.encoder = Encoder(intermediate_dim=intermediate_dim) | |
self.decoder = Decoder(intermediate_dim=intermediate_dim, original_dim=original_dim) | |
def call(self, input_features): | |
code = self.encoder(input_features) | |
reconstructed = self.decoder(code) | |
return reconstructed |
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class Decoder(tf.keras.layers.Layer): | |
def __init__(self, intermediate_dim, original_dim): | |
super(Decoder, self).__init__() | |
self.hidden_layer = tf.keras.layers.Dense( | |
units=intermediate_dim, | |
activation=tf.nn.relu, | |
kernel_initializer='he_uniform' | |
) | |
self.output_layer = tf.keras.layers.Dense( | |
units=original_dim, |
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class Encoder(tf.keras.layers.Layer): | |
def __init__(self, intermediate_dim): | |
super(Encoder, self).__init__() | |
self.hidden_layer = tf.keras.layers.Dense( | |
units=intermediate_dim, | |
activation=tf.nn.relu, | |
kernel_initializer='he_uniform' | |
) | |
self.output_layer = tf.keras.layers.Dense( | |
units=intermediate_dim, |
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"""TensorFlow 2.0 implementation of vanilla Autoencoder.""" | |
import numpy as np | |
import tensorflow as tf | |
__author__ = "Abien Fred Agarap" | |
np.random.seed(1) | |
tf.random.set_seed(1) | |
batch_size = 128 | |
epochs = 10 |