Add updated gan classes

2 files changed, 400 insertions, 0 deletions

diff --git a/cgan.py b/cgan.py
new file mode 100644
index 0000000..f71094c
--- /dev/null
+++ b/cgan.py
@@ -0,0 +1,214 @@
+from __future__ import print_function, division
+
+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
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+class CGAN():
+    def __init__(self):
+        # Input shape
+        self.img_rows = 28
+        self.img_cols = 28
+        self.channels = 1
+        self.img_shape = (self.img_rows, self.img_cols, self.channels)
+        self.num_classes = 10
+        self.latent_dim = 100
+
+        optimizer = Adam(0.0002, 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()
+
+        # The generator takes noise and the target label as input
+        # and generates the corresponding digit of that label
+        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 image as input and determines validity
+        # and the label of that image
+        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):
+
+        model = Sequential()
+
+        model.add(Dense(256, input_dim=self.latent_dim))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(BatchNormalization(momentum=0.8))
+        model.add(Dense(512))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(BatchNormalization(momentum=0.8))
+        model.add(Dense(1024))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(BatchNormalization(momentum=0.8))
+        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
+        model.add(Reshape(self.img_shape))
+
+        #model.summary()
+
+        noise = Input(shape=(self.latent_dim,))
+        label = Input(shape=(1,), dtype='int32')
+        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
+
+        model_input = multiply([noise, label_embedding])
+        img = model(model_input)
+
+        return Model([noise, label], img)
+
+    def build_discriminator(self):
+
+        model = Sequential()
+
+        model.add(Dense(512, input_dim=np.prod(self.img_shape)))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(Dense(512))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(Dropout(0.4))
+        model.add(Dense(512))
+        model.add(LeakyReLU(alpha=0.2))
+        model.add(Dropout(0.4))
+        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):
+
+        # 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 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)
+            d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], 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))
+            if epoch % 500 == 0:
+              print(epoch)
+            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)
+        dummy_labels = np.zeros(32).reshape(-1, 1)
+        
+        gen_imgs = self.generator.predict([noise, dummy_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, (60000, 100))
+      noise_test = np.random.normal(0, 1, (10000, 100))
+
+      gen_train = np.zeros(60000).reshape(-1, 1)
+      gen_test = np.zeros(10000).reshape(-1, 1)
+      for i in range(10):
+        gen_train[i*600:] = i
+        gen_test[i*100:] = i
+
+      return self.generator.predict([noise_train, gen_train]), self.generator.predict([noise_test, gen_test]), gen_train, gen_test
+
+
+'''
+if __name__ == '__main__':
+    cgan = CGAN()
+    cgan.train(epochs=7000, batch_size=32, sample_interval=200)
+    train, test, tr_labels, te_labels = cgan.generate_data()
+    print(train.shape, test.shape)
+'''
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diff --git a/dcgan.py b/dcgan.py
new file mode 100644
index 0000000..b48e99f
--- /dev/null
+++ b/dcgan.py
@@ -0,0 +1,186 @@
+from __future__ import print_function, division
+from keras.datasets import mnist
+from keras.layers import Input, Dense, Reshape, Flatten, Dropout
+from keras.layers import BatchNormalization, Activation, 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
+
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+import sys
+
+import numpy as np
+
+class DCGAN():
+    def __init__(self):
+        # Input shape
+        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
+
+        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()
+
+        # The generator takes noise as input and generates imgs
+        z = Input(shape=(self.latent_dim,))
+        img = self.generator(z)
+
+        # 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)
+
+        # The combined model  (stacked generator and discriminator)
+        # Trains the generator to fool the discriminator
+        self.combined = Model(z, valid)
+        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
+
+    def build_generator(self):
+
+        model = Sequential()
+
+        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
+        model.add(Reshape((7, 7, 128)))
+        model.add(UpSampling2D())
+        model.add(Conv2D(128, kernel_size=3, padding="same"))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
+        model.add(UpSampling2D())
+        model.add(Conv2D(64, kernel_size=3, padding="same"))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
+        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
+        model.add(Activation("tanh"))
+
+        #model.summary()
+
+        noise = Input(shape=(self.latent_dim,))
+        img = model(noise)
+
+        return Model(noise, img)
+
+    def build_discriminator(self):
+
+        model = Sequential()
+
+        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, 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)
+        validity = model(img)
+
+        return Model(img, validity)
+
+    def train(self, epochs, batch_size=128, save_interval=50):
+
+        # Load the dataset
+        (X_train, _), (_, _) = mnist.load_data()
+
+        # Rescale -1 to 1
+        X_train = X_train / 127.5 - 1.
+        X_train = np.expand_dims(X_train, axis=3)
+
+        # 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 range(epochs):
+
+            # ---------------------
+            #  Train Discriminator
+            # ---------------------
+
+            # Select a random half of images
+            idx = np.random.randint(0, X_train.shape[0], batch_size)
+            imgs = X_train[idx]
+
+            # Sample noise and generate a batch of new images
+            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
+            gen_imgs = self.generator.predict(noise)
+
+            # Train the discriminator (real classified as ones and generated as zeros)
+            d_loss_real = self.discriminator.train_on_batch(imgs, valid)
+            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
+            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
+
+            # ---------------------
+            #  Train Generator
+            # ---------------------
+
+            # Train the generator (wants discriminator to mistake images as real)
+            g_loss = self.combined.train_on_batch(noise, valid)
+
+            # Plot the progress
+            #print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
+            if epoch % 500 == 0:
+              print(epoch)
+            loss[0][epoch] = d_loss[0]
+            loss[1][epoch] = g_loss
+            # If at save interval => save generated image samples
+            if epoch % save_interval == 0:
+                self.save_imgs(epoch)
+        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 save_imgs(self, epoch):
+        r, c = 10, 10
+        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
+        gen_imgs = self.generator.predict(noise)
+
+        # Rescale images 0 - 1
+        gen_imgs = 0.5 * gen_imgs + 0.5
+
+        fig, axs = plt.subplots(r, c)
+        gs = gridspec.GridSpec(r, c)
+        gs.update(wspace=0.05, hspace=0.05)
+        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].axis('off')
+                cnt += 1
+        fig.savefig("images/mnist_%d.png" % epoch)
+        plt.close()
+
+'''
+if __name__ == '__main__':
+    dcgan = DCGAN()
+    dcgan.train(epochs=4000, batch_size=32, save_interval=50)
+'''
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