Rewrite cdcgan

1 files changed, 93 insertions, 78 deletions

diff --git a/cdcgan.py b/cdcgan.py
index aba3669..8a4b168 100755
--- a/cdcgan.py
+++ b/cdcgan.py
@@ -1,45 +1,46 @@
 from __future__ import print_function, division
-import tensorflow.keras as keras
+import tensorflow as keras
+
 import tensorflow as tf
-from keras.datasets import mnist
-
-import keras.layers as layers
-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, Embedding, Activation, 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
+import matplotlib.gridspec as gridspec
+
 from tqdm import tqdm
 
+import sys
+
 import numpy as np
 
 class CDCGAN():
-    def __init__(self):
+    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.num_classes = 10
         self.latent_dim = 100
+        self.conv_layers = conv_layers
 
-        optimizer = Adam(0.0002, 0.5)
+        optimizer = Adam(0.002, 0.5)
 
         # Build and compile the discriminator
         self.discriminator = self.build_discriminator()
-
-        self.discriminator.compile(loss=['binary_crossentropy'],
+        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])
@@ -47,10 +48,10 @@ class CDCGAN():
         # 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
+        # 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)
@@ -58,61 +59,78 @@ class CDCGAN():
             optimizer=optimizer)
 
     def build_generator(self):
-        # Prepare noise input
-        input_z = layers.Input((100,))
-        dense_z_1 = layers.Dense(1024)(input_z)
-        act_z_1 = layers.Activation("tanh")(dense_z_1)
-        dense_z_2 = layers.Dense(128 * 7 * 7)(act_z_1)
-        bn_z_1 = layers.BatchNormalization()(dense_z_2)
-        reshape_z = layers.Reshape((7, 7, 128), input_shape=(128 * 7 * 7,))(bn_z_1)
-
-        # Prepare Conditional (label) input
-        input_c = layers.Input((1,))
-        dense_c_1 = layers.Dense(1024)(input_c)
-        act_c_1 = layers.Activation("tanh")(dense_c_1)
-        dense_c_2 = layers.Dense(128 * 7 * 7)(act_c_1)
-        bn_c_1 = layers.BatchNormalization()(dense_c_2)
-        reshape_c = layers.Reshape((7, 7, 128), input_shape=(128 * 7 * 7,))(bn_c_1)
-
-        # Combine input source
-        concat_z_c = layers.Concatenate()([reshape_z, reshape_c])
-
-        # Image generation with the concatenated inputs
-        up_1 = layers.UpSampling2D(size=(2, 2))(concat_z_c)
-        conv_1 = layers.Conv2D(64, (5, 5), padding='same')(up_1)
-        act_1 = layers.Activation("tanh")(conv_1)
-        up_2 = layers.UpSampling2D(size=(2, 2))(act_1)
-        conv_2 = layers.Conv2D(1, (5, 5), padding='same')(up_2)
-        act_2 = layers.Activation("tanh")(conv_2)
-        model = Model(inputs=[input_z, input_c], outputs=act_2)
-        return model
 
+        model = Sequential()
+
+        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
+        model.add(Reshape((7, 7, 128)))
+        model.add(UpSampling2D())
+
+        for i in range(self.conv_layers):
+            model.add(Conv2D(128, kernel_size=3, padding="same"))
+            model.add(BatchNormalization())
+            model.add(Activation("relu"))
+
+        model.add(UpSampling2D())
+
+        for i in range(self.conv_layers):
+            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,))
+        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):
-        input_gen_image = layers.Input((28, 28, 1))
-        conv_1_image = layers.Conv2D(64, (5, 5), padding='same')(input_gen_image)
-        act_1_image = layers.Activation("tanh")(conv_1_image)
-        pool_1_image = layers.MaxPooling2D(pool_size=(2, 2))(act_1_image)
-        conv_2_image = layers.Conv2D(128, (5, 5))(pool_1_image)
-        act_2_image = layers.Activation("tanh")(conv_2_image)
-        pool_2_image = layers.MaxPooling2D(pool_size=(2, 2))(act_2_image)
-
-        input_c = layers.Input((1,))
-        dense_1_c = layers.Dense(1024)(input_c)
-        act_1_c = layers.Activation("tanh")(dense_1_c)
-        dense_2_c = layers.Dense(5 * 5 * 128)(act_1_c)
-        bn_c = layers.BatchNormalization()(dense_2_c)
-        reshaped_c = layers.Reshape((5, 5, 128))(bn_c)
-
-        concat = layers.Concatenate()([pool_2_image, reshaped_c])
-
-        flat = layers.Flatten()(concat)
-        dense_1 = layers.Dense(1024)(flat)
-        act_1 = layers.Activation("tanh")(dense_1)
-        dense_2 = layers.Dense(1)(act_1)
-        act_2 = layers.Activation('sigmoid')(dense_2)
-        model = Model(inputs=[input_gen_image, input_c], outputs=act_2)
-        return model
+
+        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):
 
@@ -143,6 +161,7 @@ class CDCGAN():
             # 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])
 
@@ -224,10 +243,6 @@ class CDCGAN():
 
       return train_data, test_data, val_data, labels_train, labels_test, labels_val
 
-'''
 if __name__ == '__main__':
-    cgan = CDCGAN()
-    cgan.train(epochs=70, batch_size=32, sample_interval=200)
-    train, test, tr_labels, te_labels = cgan.generate_data()
-    print(train.shape, test.shape)
-'''
+    cdcgan = CDCGAN()
+    cdcgan.train(epochs=4000, batch_size=32)