diff options
Diffstat (limited to 'cgan.py')
| -rw-r--r--[-rwxr-xr-x] | cgan.py | 28 |
1 files changed, 17 insertions, 11 deletions
@@ -1,21 +1,23 @@ from __future__ import print_function, division import tensorflow.keras as keras import tensorflow as tf -from keras.datasets import mnist -from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply -from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D -from keras.layers.advanced_activations import LeakyReLU -from keras.layers.convolutional import UpSampling2D, Conv2D -from keras.models import Sequential, Model -from keras.optimizers import Adam +from tensorflow.keras.datasets import mnist +from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply +from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D +from tensorflow.keras.layers import LeakyReLU +from tensorflow.keras.layers import UpSampling2D, Conv2D +from tensorflow.keras.models import Sequential, Model +from tensorflow.keras.optimizers import Adam import matplotlib.pyplot as plt from IPython.display import clear_output from tqdm import tqdm +from lib.virtual_batch import VirtualBatchNormalization + import numpy as np class CGAN(): - def __init__(self, dense_layers = 3): + def __init__(self, dense_layers = 3, virtual_batch_normalization=False): # Input shape self.img_rows = 28 self.img_cols = 28 @@ -24,6 +26,7 @@ class CGAN(): self.num_classes = 10 self.latent_dim = 100 self.dense_layers = dense_layers + self.virtual_batch_normalization = virtual_batch_normalization optimizer = Adam(0.0002, 0.5) @@ -63,7 +66,10 @@ class CGAN(): output_size = 2**(8+i) model.add(Dense(output_size, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) - model.add(BatchNormalization(momentum=0.8)) + if self.virtual_batch_normalization: + model.add(VirtualBatchNormalization(momentum=0.8)) + else: + model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) @@ -136,6 +142,7 @@ class CGAN(): # Sample noise as generator input noise = np.random.normal(0, 1, (batch_size, 100)) + tf.keras.backend.get_session().run(tf.global_variables_initializer()) # Generate a half batch of new images gen_imgs = self.generator.predict([noise, labels]) @@ -217,10 +224,9 @@ class CGAN(): return train_data, test_data, val_data, labels_train, labels_test, labels_val - ''' if __name__ == '__main__': - cgan = CGAN(dense_layers=1) + cgan = CGAN(dense_layers=1, virtual_batch_normalization=True) cgan.train(epochs=7000, batch_size=32, sample_interval=200) train, test, tr_labels, te_labels = cgan.generate_data() print(train.shape, test.shape) |