How to custom build a convolution generator network? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsLeNet 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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How to custom build a convolution generator network?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsLeNet 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?
$begingroup$
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?
deep-learning keras convnet gan generative-models
asked Apr 5 at 5:37
EkaEka
1024
$endgroup$
add a comment |
$begingroup$
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?
deep-learning keras convnet gan generative-models
asked Apr 5 at 5:37
EkaEka
1024
$endgroup$
add a comment |
$begingroup$
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?
deep-learning keras convnet gan generative-models
asked Apr 5 at 5:37
EkaEka
1024
$endgroup$
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
deep-learning keras convnet gan generative-models
asked Apr 5 at 5:37
EkaEka
1024
asked Apr 5 at 5:37
EkaEka
1024
asked Apr 5 at 5:37
EkaEka
1024
asked Apr 5 at 5:37
EkaEka
1024
asked Apr 5 at 5:37
EkaEka
1024
1024
add a comment |
add a comment |
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