How to custom build a convolution generator network?
$begingroup$
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 11 mins ago
EkaEka
1023
$endgroup$
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 11 mins ago
EkaEka
1023
$endgroup$
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 11 mins ago
EkaEka
1023
$endgroup$
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 11 mins ago
EkaEka
1023
asked 11 mins ago
EkaEka
1023
asked 11 mins ago
EkaEka
1023
asked 11 mins ago
EkaEka
1023
asked 11 mins ago
EkaEka
1023
1023
add a comment |
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