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-rwxr-xr-xncdcgan.py258
1 files changed, 258 insertions, 0 deletions
diff --git a/ncdcgan.py b/ncdcgan.py
new file mode 100755
index 0000000..7aa7e83
--- /dev/null
+++ b/ncdcgan.py
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+from __future__ import print_function, division
+import tensorflow as keras
+
+import tensorflow as tf
+import tensorflow.keras as keras
+from keras.datasets import mnist
+from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
+from keras.layers import BatchNormalization, Embedding, Activation, ZeroPadding2D
+from keras.layers import LeakyReLU
+from keras.layers import UpSampling2D, Conv2D, Conv2DTranspose
+from keras.models import Sequential, Model
+from keras.optimizers import Adam
+
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+from tqdm import tqdm
+
+import sys
+
+import numpy as np
+
+class nCDCGAN():
+ def __init__(self, conv_layers = 1, num_classes = 10):
+ # Input shape
+ self.num_classes = num_classes
+ self.img_rows = 28
+ self.img_cols = 28
+ self.channels = 1
+ self.img_shape = (self.img_rows, self.img_cols, self.channels)
+ self.latent_dim = 100
+ self.conv_layers = conv_layers
+
+ optimizer = Adam(0.002, 0.5)
+
+ # Build and compile the discriminator
+ self.discriminator = self.build_discriminator()
+ self.discriminator.compile(loss='binary_crossentropy',
+ optimizer=optimizer,
+ metrics=['accuracy'])
+
+ # Build the generator
+ self.generator = self.build_generator()
+
+ noise = Input(shape=(self.latent_dim,))
+ label = Input(shape=(1,))
+ img = self.generator([noise, label])
+
+ # For the combined model we will only train the generator
+ self.discriminator.trainable = False
+
+ # The discriminator takes generated images as input and determines validity
+ valid = self.discriminator([img, label])
+
+
+ # The combined model (stacked generator and discriminator)
+ # Trains generator to fool discriminator
+ self.combined = Model([noise, label], valid)
+ self.combined.compile(loss=['binary_crossentropy'],
+ optimizer=optimizer)
+
+ def build_generator(self):
+ noise = Input(shape=(self.latent_dim,))
+ label = Input(shape=(1,), dtype='int32')
+ label = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
+
+ noise = Dense(7 * 7 * 256)(noise)
+ noise = Reshape(target_shape=(7, 7, 256))(noise)
+ noise = Conv2DTranspose(256, kernel_size=3, padding="same")(noise)
+ noise = BatchNormalization()(noise)
+ noise = Activation("relu")(noise)
+
+ label = Dense(7 * 7 * 256)(label)
+ label = Reshape(target_shape=(7, 7, 256))(label)
+ label = Conv2DTranspose(256, kernel_size=3, padding="same")(label)
+ label = BatchNormalization()(label)
+ label = Activation("relu")(label)
+
+ # Combine the two
+ x = keras.layers.Concatenate()([noise, label])
+
+ x = Conv2DTranspose(256, kernel_size=3, padding="same", strides=(2,2))(x)
+ x = BatchNormalization()(x)
+ x = Activation("relu")(x)
+
+ x = Conv2DTranspose(128, kernel_size=3, padding="same", strides=(2,2))(x)
+ x = BatchNormalization()(x)
+ x = Activation("relu")(x)
+
+ x = Conv2DTranspose(64, kernel_size=3, padding="same", strides=(2,2))(x)
+ x = BatchNormalization()(x)
+ x = Activation("relu")(x)
+
+ x = (Conv2DTranspose(1, kernel_size=3, padding="same"))(x)
+ x = Activation("tanh")(x)
+
+ model = Model(inputs=[noise, label], outputs=x)
+
+ model.summary()
+
+ return model
+
+ def build_discriminator(self):
+
+ model = Sequential()
+
+ model.add(Dense(28 * 28 * 3, activation="relu"))
+ model.add(Reshape((28, 28, 3)))
+ model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
+ model.add(ZeroPadding2D(padding=((0,1),(0,1))))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Flatten())
+ model.add(Dense(1, activation='sigmoid'))
+
+ #model.summary()
+
+ img = Input(shape=self.img_shape)
+
+ label = Input(shape=(1,), dtype='int32')
+
+ label_embedding = Flatten()(Embedding(self.num_classes, np.prod(self.img_shape))(label))
+ flat_img = Flatten()(img)
+
+ model_input = multiply([flat_img, label_embedding])
+
+ validity = model(model_input)
+
+ return Model([img, label], validity)
+
+ def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth_real=1, smooth_fake=0):
+
+ # Load the dataset
+ (X_train, y_train), (_, _) = mnist.load_data()
+
+ # Configure input
+ X_train = (X_train.astype(np.float32) - 127.5) / 127.5
+ X_train = np.expand_dims(X_train, axis=3)
+ y_train = y_train.reshape(-1, 1)
+
+ # Adversarial ground truths
+ valid = np.ones((batch_size, 1))
+ fake = np.zeros((batch_size, 1))
+
+ xaxis = np.arange(epochs)
+ loss = np.zeros((2,epochs))
+ for epoch in tqdm(range(epochs)):
+
+ # ---------------------
+ # Train Discriminator
+ # ---------------------
+
+ # Select a random half batch of images
+ idx = np.random.randint(0, X_train.shape[0], batch_size)
+ imgs, labels = X_train[idx], y_train[idx]
+
+ # Sample noise as generator input
+ noise = np.random.normal(0, 1, (batch_size, 100))
+
+ # Generate a half batch of new images
+ gen_imgs = self.generator.predict([noise, labels])
+
+ # Train the discriminator
+ d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*smooth_real)
+ d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], valid*smooth_fake)
+ d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
+
+ # ---------------------
+ # Train Generator
+ # ---------------------
+
+ # Condition on labels
+ sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
+ # Train the generator
+ g_loss = self.combined.train_on_batch([noise, sampled_labels], valid)
+
+ # Plot the progress
+ #print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
+ loss[0][epoch] = d_loss[0]
+ loss[1][epoch] = g_loss
+
+ # If at save interval => save generated image samples
+ if epoch % sample_interval == 0:
+ self.sample_images(epoch)
+
+ if graph:
+ plt.plot(xaxis,loss[0])
+ plt.plot(xaxis,loss[1])
+ plt.legend(('Discriminator', 'Generator'), loc='best')
+ plt.xlabel('Epoch')
+ plt.ylabel('Binary Crossentropy Loss')
+
+ def sample_images(self, epoch):
+ r, c = 2, 5
+ noise = np.random.normal(0, 1, (r * c, 100))
+ sampled_labels = np.arange(0, 10).reshape(-1, 1)
+
+ #using dummy_labels would just print zeros to help identify image quality
+ #dummy_labels = np.zeros(32).reshape(-1, 1)
+
+ gen_imgs = self.generator.predict([noise, sampled_labels])
+
+ # Rescale images 0 - 1
+ gen_imgs = 0.5 * gen_imgs + 0.5
+
+ fig, axs = plt.subplots(r, c)
+ cnt = 0
+ for i in range(r):
+ for j in range(c):
+ axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
+ axs[i,j].set_title("Digit: %d" % sampled_labels[cnt])
+ axs[i,j].axis('off')
+ cnt += 1
+ fig.savefig("images/%d.png" % epoch)
+ plt.close()
+
+ def generate_data(self):
+ noise_train = np.random.normal(0, 1, (55000, 100))
+ noise_test = np.random.normal(0, 1, (10000, 100))
+ noise_val = np.random.normal(0, 1, (5000, 100))
+
+ labels_train = np.zeros(55000).reshape(-1, 1)
+ labels_test = np.zeros(10000).reshape(-1, 1)
+ labels_val = np.zeros(5000).reshape(-1, 1)
+
+ for i in range(10):
+ labels_train[i*5500:-1] = i
+ labels_test[i*1000:-1] = i
+ labels_val[i*500:-1] = i
+
+ train_data = self.generator.predict([noise_train, labels_train])
+ test_data = self.generator.predict([noise_test, labels_test])
+ val_data = self.generator.predict([noise_val, labels_val])
+
+ labels_train = keras.utils.to_categorical(labels_train, 10)
+ labels_test = keras.utils.to_categorical(labels_test, 10)
+ labels_val = keras.utils.to_categorical(labels_val, 10)
+
+ return train_data, test_data, val_data, labels_train, labels_test, labels_val
+
+'''
+if __name__ == '__main__':
+ cdcgan = nCDCGAN()
+ cdcgan.train(epochs=4000, batch_size=32)
+'''
+