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conv_ae_tf.py
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conv_ae_tf.py
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# convolutional autoencoder
# Based on
# https://github.com/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow import keras
devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(devices[0], True)
(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
X_train_full = X_train_full.astype(np.float32) / 255
X_test = X_test.astype(np.float32) / 255
X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]
y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]
tf.random.set_seed(42)
np.random.seed(42)
conv_encoder = keras.models.Sequential([
keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
keras.layers.Conv2D(16, kernel_size=3, padding="SAME", activation="selu"),
keras.layers.MaxPool2D(pool_size=2),
keras.layers.Conv2D(32, kernel_size=3, padding="SAME", activation="selu"),
keras.layers.MaxPool2D(pool_size=2),
keras.layers.Conv2D(64, kernel_size=3, padding="SAME", activation="selu"),
keras.layers.MaxPool2D(pool_size=2)
])
conv_decoder = keras.models.Sequential([
keras.layers.Conv2DTranspose(32, kernel_size=3, strides=2, padding="VALID", activation="selu",
input_shape=[3, 3, 64]),
keras.layers.Conv2DTranspose(16, kernel_size=3, strides=2, padding="SAME", activation="selu"),
keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding="SAME", activation="sigmoid"),
keras.layers.Reshape([28, 28])
])
conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
def rounded_accuracy(y_true, y_pred):
return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))
conv_ae.compile(loss="binary_crossentropy", optimizer=keras.optimizers.SGD(lr=1.0),
metrics=[rounded_accuracy])
history = conv_ae.fit(X_train, X_train, epochs=5,
validation_data=[X_valid, X_valid])
def plot_image(image):
plt.imshow(image, cmap="binary")
plt.axis("off")
def show_reconstructions(model, images=X_valid, n_images=5):
reconstructions = model.predict(images[:n_images])
plt.figure(figsize=(n_images * 1.5, 3))
for image_index in range(n_images):
plt.subplot(2, n_images, 1 + image_index)
plot_image(images[image_index])
plt.subplot(2, n_images, 1 + n_images + image_index)
plot_image(reconstructions[image_index])
show_reconstructions(conv_ae)
#save_fig("ae-cnn-fashion-recon.pdf")
plt.show()