def test_autoencoder():
"""
Test that all components of the auto-encoder work correctly by executing a
training run against generated data.
"""
input_shape = (3, )
epochs = 1000
# Generate some data
x_train = np.random.rand(100, 3)
x_test = np.random.rand(30, 3)
# Define encoder and decoder model
def create_encoder_model(input_shape):
model_input = Input(shape=input_shape)
encoder = Dense(4)(model_input)
encoder = BatchNormalization()(encoder)
encoder = Activation(activation='relu')(encoder)
return Model(model_input, encoder)
def create_decoder_model(embedding_shape):
embedding_a = Input(shape=embedding_shape)
decoder = Dense(3)(embedding_a)
decoder = BatchNormalization()(decoder)
decoder = Activation(activation='relu')(decoder)
return Model(embedding_a, decoder)
# Create auto-encoder network
encoder_model = create_encoder_model(input_shape)
decoder_model = create_decoder_model(encoder_model.output_shape)
autoencoder = AutoEncoder(encoder_model, decoder_model)
# Prepare auto-encoder for training
autoencoder.compile(loss='binary_crossentropy', optimizer='adam')
# Evaluate network before training to establish a baseline
score_before = autoencoder.evaluate(x_train, x_train)
# Train network
autoencoder.fit(x_train,
x_train,
validation_data=(x_test, x_test),
epochs=epochs)
# Evaluate network
score_after = autoencoder.evaluate(x_train, x_train)
# Ensure that the training loss score improved as a result of the training
assert (score_before > score_after)
# Normalize all images
print("\nNormalizing training images")
imgs_train = normalize_img(imgs_train)
print("Number of training images:", len(imgs_train))
# Convert images to numpy array of right dimensions
print("\nConverting training images to numpy array of right dimensions")
X_train = np.array(imgs_train).reshape((-1, ) + input_shape_model)
print(">>> X_train.shape = " + str(X_train.shape))
print("Number of training images:", len(X_train))
# Create object for train augmentation
completeTrainGen = data_augmentation(X_train, args.bs)
print("\nStart training...")
# Compiling
model.compile(loss=args.loss, optimizer="adam")
# Fitting
model.fit(completeTrainGen,
steps_per_epoch,
n_epochs=args.e,
batch_size=args.bs,
wandb=args.wandb)
# Saving
model.save_models()
print("Done training")
print("\nCreating embeddings...")
E_train = model.predict(X_train)
E_train_flatten = E_train.reshape((-1, np.prod(output_shape_model)))
if __name__ == "__main__":
# load MNIST dataset
mnist = tf.keras.datasets.mnist
# extract train and val data
(x_train, _),(x_val, _) = mnist.load_data()
# reshape and normalize in range [0 .. 1]
x_train, x_val = x_train.reshape(-1, 28*28) / 255.0, x_val.reshape(-1, 28*28) / 255.0
# init model
model = AutoEncoder(z_dim=32)
# set loss and optimizer type
model.compile(optimizer='adam', loss='mean_squared_error')
# train model
model.fit(x_train, x_train, batch_size=32, epochs=20, verbose=1, validation_data=(x_val, x_val))
# show some results
# =================== PLOTTING ============================
# images per row and col
NUM_IMG_PER_ROW = 10
# to store images
selected_imgs = []
# pick random indexes to visualize
indexes = np.random.random_integers(x_train.shape[0], size=(1,NUM_IMG_PER_ROW**2))
decoder = Dense(1 * 28 * 28)(embedding_a)
decoder = BatchNormalization()(decoder)
decoder = Activation(activation='relu')(decoder)
decoder = Reshape(input_shape)(decoder)
return Model(embedding_a, decoder)
num_classes = 10
epochs = 999999
encoder_model = create_encoder_model(input_shape)
decoder_model = create_decoder_model(encoder_model.output_shape)
autoencoder_network = AutoEncoder(encoder_model, decoder_model)
autoencoder_network.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.adam(),
metrics=['accuracy'])
autoencoder_checkpoint_path = "./autoencoder_checkpoint"
autoencoder_callbacks = [
EarlyStopping(monitor='val_acc', patience=10, verbose=0),
ModelCheckpoint(autoencoder_checkpoint_path,
monitor='val_acc',
save_best_only=True,
verbose=0)
]
autoencoder_network.fit(x_train,
x_train,
validation_data=(x_test, x_test),