WIP

models/autoencoder.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+from sklearn.metrics import accuracy_score
+from tensorflow.keras import layers, losses
+from tensorflow.keras.models import Model
+import misc
+
+
+latent_dim = 64
+
+# print("# GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
+
+
+class Autoencoder(Model):
+    def __init__(self, nary):
+        super(Autoencoder, self).__init__()
+        self.latent_dim = latent_dim
+        self.encoder = tf.keras.Sequential()
+        self.encoder.add(tf.keras.Input(shape=(4,)))
+        self.encoder.add(layers.Dense(units=8, activation='relu'))
+        # self.encoder.add(layers.Dropout(0.2))
+        # self.encoder.add(layers.ReLU(max_value=1.0))
+
+        self.decoder = tf.keras.Sequential()
+        self.decoder.add(tf.keras.Input(shape=(2,)))
+        self.decoder.add(layers.Dense(units=8))
+        # self.encoder.add(tf.keras.layers.Dropout(0.2))
+        self.decoder.add(layers.Dense(units=4, activation='softmax'))
+        # self.decoder.add(layers.Softmax(units=4, dtype=bool))
+
+        # [
+        #     layers.Input(shape=(28, 28, 1)),
+        #     layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=2),
+        #     layers.Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)
+        # ])
+        # self.decoder = tf.keras.Sequential([
+        #     layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),
+        #     layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),
+        #     layers.Conv2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same')
+        # ])
+
+    def call(self, x, **kwargs):
+        encoded = self.encoder(x)
+        decoded = self.decoder(encoded)
+        return decoded
+
+
+if __name__ == '__main__':
+    # (x_train, _), (x_test, _) = fashion_mnist.load_data()
+    #
+    # x_train = x_train.astype('float32') / 255.
+    # x_test = x_test.astype('float32') / 255.
+    #
+    # print(f"Train data: {x_train.shape}")
+    # print(f"Test data: {x_test.shape}")
+
+    x_train = misc.generate_random_bit_array(1e5).reshape((-1, 4))
+    x_test = misc.generate_random_bit_array(1e4).reshape((-1, 4))
+
+    autoencoder = Autoencoder(latent_dim)
+    autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())
+
+    autoencoder.fit(x_train, x_train,
+                    epochs=1,
+                    shuffle=True,
+                    validation_data=(x_test, x_test))
+
+    encoded_data = autoencoder.encoder(x_test)
+    decoded_data = autoencoder.decoder(encoded_data)
+
+    print(accuracy_score(x_test, encoded_data))
+    pass