現在kerasチームが公開しているconditional GANのコード<https://github.com/eriklindernoren/Keras-
GAN/blob/master/cgan/cgan.py>を元にし，1次元データを入力とするConditional
GANの実装を目指しています．そこで，Discriminatorの識別精度が100%近くになり，良いデータを生成できないという状態になってしまいました．状況を打開する心当たりがあれば，よろしくお願いします．元のコードからの変更箇所はコメントアウトで示しています．

入力データは(1087*1000)の数値データと1001列目にクラスラベル(14クラス)を記述したsam.csvです．  
sam.csvの中身は，データ数が多い方がいいかと考え，14行分のデータをコピペして1087行まで増やしたものです．

元はmnistなど2次元向けのモデルに対して，1*1000の形で入力しているのが問題なのでしょうか  
もしくは同じ1次元データを含んでいる入力データが問題なのでしょうか

```

    from __future__ import print_function, division
    import os
    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 csv
    import warnings;warnings.filterwarnings("ignore")
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    import matplotlib.pyplot as plt
    plt.rcParams.update({'figure.max_open_warning':0})
    import numpy as np
    
    class CGAN():
        def __init__(self, epochs, batch_size, sample_interval):
            # Input shape
            self.img_rows = 1 ##28から変更##
            self.img_cols = 1000　##28から変更##
            self.channels = 1
            self.img_shape = (self.img_rows, self.img_cols,self.channels)
            self.num_classes = 14　##10から変更##
            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='float32')
            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='float32')
    
            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):
            d_loss_prog = []
            g_loss_prog = []
    
           ######### 使用データの変更#############
            with open('./sam.csv', 'r') as f:
                reader = csv.reader(f)
                data_len = 1000
                data_num = 1089
                y_train = []
                x_train = []
                for row in reader:
                    y_train_value = row[-1]
                    y_train.append(y_train_value)
                    x_train_value = row[0:data_len]
                    x_train.append(x_train_value)
                x_train = np.array(x_train)
                x_train = x_train.reshape(data_num,1,data_len)
                y_train = np.array(y_train)
                y_train = y_train.reshape(-1, 1)
                x_train = x_train.astype(np.float32)
                x_mean = x_train.mean()
                x_train2 = x_train - x_mean
                x_std = x_train2.std()
                X_train = x_train2 / x_std**
                X_train = np.expand_dims(X_train, axis=3)
    
             #############　ここまで変更 ###################
    
        # 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))
        half_batch = int(batch_size/2)
        for epoch in range(epochs):
    
            # ---------------------
            #  Train Discriminator
            # ---------------------
    
            # Select a random half batch of images
            idx = np.random.randint(1, X_train.shape[0]+1, half_batch)
    
            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, 14, batch_size).reshape(-1, 1)　##10から14に変更##
            # Train the generator
            g_loss = self.combined.train_on_batch([noise, sampled_labels], valid)
    
            # Plot the progress
    
    
            if epoch % sample_interval == 0:
                print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
                d_loss_prog.append(d_loss)
                g_loss_prog.append(g_loss)
                plt.plot(d_loss_prog)
                plt.plot(g_loss_prog)
                plt.pause(0.01)
                self.generator.save_weights('generator', overwrite=True)
                self.discriminator.save_weights('discriminator', overwrite=True)
    
    
        def generate_images(self, label):
            for i in range(2):
                self.generator.load_weights('./generator')
                noise = np.random.normal(0, 1, (1, self.latent_dim))
                gen_imgs = self.generator.predict([noise, np.array(label).reshape(-1,1)])
                np.savetxt('outp.csv', gen_imgs[0,:,:,0], delimiter=',')
    
    
    if __name__ == '__main__':
        epochs = 5000
        batch_size = 32
        sample_interval =50
    
        cgan = CGAN(epochs, batch_size, sample_interval)
        cgan.train(epochs, batch_size, sample_interval)
       #cgan.generate_images(1) # generate images of a specific class
    
```

[![学習曲線](https://i.stack.imgur.com/mg5IE.png)](https://i.stack.imgur.com/mg5IE.png)
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2019-01-22T04:43:54.140
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2019-01-22T05:28:51.900
2019-01-22T05:28:51.900
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question
python 機械学習 深層学習
Conditional GANの学習がうまくいかない