Datasets:
HenryAI
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KerasCodeExamples.txt

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def display(array1, array2):
\"\"\"
Displays ten random images from each one of the supplied arrays.
\"\"\"
n = 10
indices = np.random.randint(len(array1), size=n)
images1 = array1[indices, :]
images2 = array2[indices, :]
plt.figure(figsize=(20, 4))
for i, (image1, image2) in enumerate(zip(images1, images2)):
ax = plt.subplot(2, n, i + 1)
plt.imshow(image1.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(image2.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
Prepare the data
# Since we only need images from the dataset to encode and decode, we
# won't use the labels.
(train_data, _), (test_data, _) = mnist.load_data()
# Normalize and reshape the data
train_data = preprocess(train_data)
test_data = preprocess(test_data)
# Create a copy of the data with added noise
noisy_train_data = noise(train_data)
noisy_test_data = noise(test_data)
# Display the train data and a version of it with added noise
display(train_data, noisy_train_data)
png
Build the autoencoder
We are going to use the Functional API to build our convolutional autoencoder.
input = layers.Input(shape=(28, 28, 1))
# Encoder
x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(input)
x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)
x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(x)
x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)
# Decoder
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)
x = layers.Conv2D(1, (3, 3), activation=\"sigmoid\", padding=\"same\")(x)
# Autoencoder
autoencoder = Model(input, x)
autoencoder.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")
autoencoder.summary()
Model: \"model\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 28, 28, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 14, 14, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32) 0
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 14, 14, 32) 9248
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 28, 28, 32) 9248
_________________________________________________________________
conv2d_2 (Conv2D) (None, 28, 28, 1) 289
=================================================================
Total params: 28,353
Trainable params: 28,353
Non-trainable params: 0
_________________________________________________________________
Now we can train our autoencoder using train_data as both our input data and target. Notice we are setting up the validation data using the same format.
autoencoder.fit(
x=train_data,
y=train_data,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(test_data, test_data),
)
Epoch 1/50
469/469 [==============================] - 20s 43ms/step - loss: 0.1354 - val_loss: 0.0735