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Added random reconstruction #1

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Binary file added saved_weights/vae10.h5
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Binary file added saved_weights/vae20.h5
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60 changes: 58 additions & 2 deletions src/graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import sys
import cv2
import numpy as np
import random

from network import VAENetwork
import matplotlib.pyplot as plt
Expand Down Expand Up @@ -31,7 +32,28 @@ def generate_latent_plot(self):
plt.show()

def generate_reconstructed_image(self):


n = 20
digit_size = 28
epsilon_std = 0.1
figure = np.zeros((digit_size * n, digit_size * n))
grid_x = np.linspace(-250, 50, n)
grid_y = np.linspace(-125, 200, n)

for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample = np.array([[xi, yi]]) * epsilon_std
x_decoded = self.decoder_model.predict(z_sample)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit

plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()

def generate_reconstructed_image(self):

n = 20
digit_size = 28
epsilon_std = 0.1
Expand All @@ -51,6 +73,40 @@ def generate_reconstructed_image(self):
plt.imshow(figure)
plt.show()

pass

def generate_reconstructed_image_random_samples(self):

n = 20
digit_size = 28
epsilon_std = 1
figure = np.zeros((digit_size * n, digit_size * n))

#Get the space of latent vectors
x_encoded = self.encoder_model.predict(self.test_data, batch_size=128)[2]
min_vals = x_encoded.min(axis=0)
max_vals = x_encoded.max(axis=0)
lv_size = len(min_vals)

# Pick a random set of 400 n-dim vectors
samples = np.zeros((n*n,lv_size))
for idx_vec in range(0,lv_size):
#samples[:,idx_vec] = random.sample(range(min_vals[idx_vec], max_vals[idx_vec]), n*n)
samples[:,idx_vec] = random.randrange(int(min_vals[idx_vec]), int(max_vals[idx_vec]), n*n)
samples[:,idx_vec] = np.array([random.randint(int(min_vals[idx_vec]),int(max_vals[idx_vec])) for x in range(n*n)])


pass
print(samples.shape)

for i in range(0,n):
for j in range(0,n):
x_decoded = self.decoder_model.predict(np.array([samples[i*n+j,:]])*epsilon_std)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit

plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()

pass
22 changes: 11 additions & 11 deletions src/network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,8 +30,8 @@ def __init__(self, input_shape):
self.model = self.create_model()

def create_model(self):


reconstructed_image = Lambda(lambda x: x*255)(self.decoder_model(self.encoder_model(self.input)[2]))

model = Model(self.input, reconstructed_image)
Expand All @@ -51,19 +51,19 @@ def create_model(self):

def generate_encoder(self):

conv_layer_1 = Conv2D(filters=VAENetwork.NUM_FILTERS,
conv_layer_1 = Conv2D(filters=VAENetwork.NUM_FILTERS,
kernel_size=VAENetwork.KERNEL_SIZE,
activation='relu',
strides=2,
padding='same')(self.input)
conv_layer_2 = Conv2D(filters=VAENetwork.NUM_FILTERS*2,

conv_layer_2 = Conv2D(filters=VAENetwork.NUM_FILTERS*2,
kernel_size=VAENetwork.KERNEL_SIZE,
activation='relu',
strides=2,
padding='same')(conv_layer_1)

# conv_layer_3 = Conv2D(filters=VAENetwork.NUM_FILTERS*4,
# conv_layer_3 = Conv2D(filters=VAENetwork.NUM_FILTERS*4,
# kernel_size=VAENetwork.KERNEL_SIZE,
# activation='relu',
# strides=2,
Expand All @@ -79,12 +79,12 @@ def generate_encoder(self):
return encoder

def generate_decoder(self):

decoder_input = Input(shape=(VAENetwork.LATENT_DIMENSIONS,))
fully_connected = Dense(self.encoder_conv_shape[1]*self.encoder_conv_shape[2]*self.encoder_conv_shape[3], activation='relu')(decoder_input)
deconv_input = Reshape((self.encoder_conv_shape[1], self.encoder_conv_shape[2], self.encoder_conv_shape[3]))(fully_connected)


deconv_layer_1 = Conv2DTranspose(filters=VAENetwork.NUM_FILTERS*4,
kernel_size=VAENetwork.KERNEL_SIZE,
activation='relu',
Expand All @@ -100,15 +100,15 @@ def generate_decoder(self):
# deconv_layer_3 = Conv2DTranspose(filters=VAENetwork.NUM_FILTERS,
# kernel_size=VAENetwork.KERNEL_SIZE,
# activation='relu',
# strides=2,
# strides=2,
# padding='same')(deconv_layer_2)

reconstructed_image = Conv2DTranspose(filters=1,
kernel_size=VAENetwork.KERNEL_SIZE,
activation='sigmoid',
strides=1,
padding='same')(deconv_layer_2)

decoder = Model(decoder_input, reconstructed_image)

decoder.summary()
Expand Down
21 changes: 7 additions & 14 deletions src/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ def main(args):
mnist_data = None
if os.path.exists(MNIST_DATA_PATH):
mnist_data = mnist.MNIST(MNIST_DATA_PATH)

else:
print("MNIST data not found in the Data/ directory. Add the MNIST data to the path: ")
print(os.path.join(os.getcwd(), '..', 'data'))
Expand All @@ -27,37 +27,30 @@ def main(args):

x_train = np.array([np.reshape(np.array(x, dtype=np.uint8), (28, 28, 1)) for x in x_train])
x_test = np.array([np.reshape(np.array(x, dtype=np.uint8), (28, 28, 1)) for x in x_test])

if not os.path.exists(MODEL_WEIGHT_DIRECTORY):
os.makedirs(MODEL_WEIGHT_DIRECTORY, exist_ok=True)

vae_obj = VAENetwork(x_train[2].shape)
vae_model = vae_obj.get_model()
# vae_obj.train(x_train, x_test)
#vae_obj.train(x_train, x_test)

# vae_obj.save_weights(MODEL_WEIGHT_DIRECTORY)
#vae_obj.save_weights(MODEL_WEIGHT_DIRECTORY)

vae_obj.load_weights(MODEL_WEIGHT_DIRECTORY)
vae_model.summary()

graph_generator = GraphGenerator(vae_obj, mnist_data)
if vae_obj.LATENT_DIMENSIONS == 2:
graph_generator.generate_latent_plot()
graph_generator.generate_reconstructed_image()


graph_generator.generate_reconstructed_image()

img = vae_model.predict(np.reshape(x_test[45], (1, 28, 28, 1)))

cv2.imshow('img', np.reshape(x_test[45],(28, 28)))
cv2.waitKey(0)
cv2.destroyAllWindows()

cv2.imshow('img', np.reshape(np.array(img, dtype=np.uint8), (28, 28)))
cv2.waitKey(0)
cv2.destroyAllWindows()
graph_generator.generate_reconstructed_image_random_samples()

if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train a VAE on top of the MNIST Data.")
parser = argparse.ArgumentParser(description="Train a VAE on top of the MNIST Data.")
args = parser.parse_args()
main(args)