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How to custom build a convolution generator network?



2019 Community Moderator ElectionLeNet for Convolution network?Tensorflow: Save History of Images from Generatorhow to get predicted class labels in convolution neural network?Using Generative Adversarial Network to generate Single ImageGAN - why doesn't the generator nullify the noise input?How can both generator and discriminator losses decrease?GAN generator output minibatch classes orderEvaluating performance of Generative Adverserial Network?How to train the generator in a recurrent GAN (Keras)Why is my generator loss function increasing with iterations?










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$begingroup$


I learned GAN's by using mnist dataset(28x28) and codes available in the web. Now I am trying to build a GAN for dataset with images containing custom channel, rows, columns. eg:(3,300,200). I have used this code as generator for mnist-GAN



nch = 200
g_input = Input(shape=[100])
H = Dense(nch*14*14, init='glorot_normal')(g_input)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = Reshape( [nch, 14, 14] )(H)
H = UpSampling2D(size=(2, 2))(H)
H = Convolution2D(nch/2, 3, 3, border_mode='same', init='glorot_uniform')(H)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = Convolution2D(nch/4, 3, 3, border_mode='same', init='glorot_uniform')(H)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)
g_V = Activation('sigmoid')(H)
generator = Model(g_input,g_V)
generator.compile(loss='binary_crossentropy', optimizer=opt)
generator.summary()


What is the right way to build a generator with custom output shape?



Is there any simple GUI tool to build and visualize convolution model like this before coding it in a coding environment like keras,tensorflow,pytorch etc?









share









$endgroup$
















    0












    $begingroup$


    I learned GAN's by using mnist dataset(28x28) and codes available in the web. Now I am trying to build a GAN for dataset with images containing custom channel, rows, columns. eg:(3,300,200). I have used this code as generator for mnist-GAN



    nch = 200
    g_input = Input(shape=[100])
    H = Dense(nch*14*14, init='glorot_normal')(g_input)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = Reshape( [nch, 14, 14] )(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Convolution2D(nch/2, 3, 3, border_mode='same', init='glorot_uniform')(H)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = Convolution2D(nch/4, 3, 3, border_mode='same', init='glorot_uniform')(H)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)
    g_V = Activation('sigmoid')(H)
    generator = Model(g_input,g_V)
    generator.compile(loss='binary_crossentropy', optimizer=opt)
    generator.summary()


    What is the right way to build a generator with custom output shape?



    Is there any simple GUI tool to build and visualize convolution model like this before coding it in a coding environment like keras,tensorflow,pytorch etc?









    share









    $endgroup$














      0












      0








      0





      $begingroup$


      I learned GAN's by using mnist dataset(28x28) and codes available in the web. Now I am trying to build a GAN for dataset with images containing custom channel, rows, columns. eg:(3,300,200). I have used this code as generator for mnist-GAN



      nch = 200
      g_input = Input(shape=[100])
      H = Dense(nch*14*14, init='glorot_normal')(g_input)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Reshape( [nch, 14, 14] )(H)
      H = UpSampling2D(size=(2, 2))(H)
      H = Convolution2D(nch/2, 3, 3, border_mode='same', init='glorot_uniform')(H)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Convolution2D(nch/4, 3, 3, border_mode='same', init='glorot_uniform')(H)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)
      g_V = Activation('sigmoid')(H)
      generator = Model(g_input,g_V)
      generator.compile(loss='binary_crossentropy', optimizer=opt)
      generator.summary()


      What is the right way to build a generator with custom output shape?



      Is there any simple GUI tool to build and visualize convolution model like this before coding it in a coding environment like keras,tensorflow,pytorch etc?









      share









      $endgroup$




      I learned GAN's by using mnist dataset(28x28) and codes available in the web. Now I am trying to build a GAN for dataset with images containing custom channel, rows, columns. eg:(3,300,200). I have used this code as generator for mnist-GAN



      nch = 200
      g_input = Input(shape=[100])
      H = Dense(nch*14*14, init='glorot_normal')(g_input)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Reshape( [nch, 14, 14] )(H)
      H = UpSampling2D(size=(2, 2))(H)
      H = Convolution2D(nch/2, 3, 3, border_mode='same', init='glorot_uniform')(H)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Convolution2D(nch/4, 3, 3, border_mode='same', init='glorot_uniform')(H)
      H = BatchNormalization(mode=2)(H)
      H = Activation('relu')(H)
      H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)
      g_V = Activation('sigmoid')(H)
      generator = Model(g_input,g_V)
      generator.compile(loss='binary_crossentropy', optimizer=opt)
      generator.summary()


      What is the right way to build a generator with custom output shape?



      Is there any simple GUI tool to build and visualize convolution model like this before coding it in a coding environment like keras,tensorflow,pytorch etc?







      deep-learning keras convnet gan generative-models





      share












      share










      share



      share










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