Created
May 16, 2019 09:31
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TensorFlow 2.0 implementation of an encoder layer for a variational autoencoder.
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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) | |
| self.sampling = Sampling() | |
| def call(self, input_features): | |
| input_features = self.input_layer(input_features) | |
| input_features = self.reshape(input_features) | |
| activation_1 = self.hidden_layer_1(input_features) | |
| activation_2 = self.hidden_layer_2(activation_1) | |
| activation_3 = self.hidden_layer_3(activation_2) | |
| z_mean = self.z_mean_layer(activation_3) | |
| z_log_var = self.z_log_var_layer(activation_3) | |
| z = self.sampling((z_mean, z_log_var)) | |
| return z_mean, z_log_var, z |
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