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- def get_disc_normal(image_shape=(64,64,3)):
- dropout_prob = 0.4
- kernel_init = 'glorot_uniform'
- dis_input = Input(shape = image_shape)
- # Conv layer 1:
- discriminator = Conv2D(filters = 64, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(dis_input)
- discriminator = LeakyReLU(0.2)(discriminator)
- # Conv layer 2:
- discriminator = Conv2D(filters = 128, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
- discriminator = BatchNormalization(momentum = 0.5)(discriminator)
- discriminator = LeakyReLU(0.2)(discriminator)
- # Conv layer 3:
- discriminator = Conv2D(filters = 256, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
- discriminator = BatchNormalization(momentum = 0.5)(discriminator)
- discriminator = LeakyReLU(0.2)(discriminator)
- # Conv layer 4:
- discriminator = Conv2D(filters = 512, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
- discriminator = BatchNormalization(momentum = 0.5)(discriminator)
- discriminator = LeakyReLU(0.2)(discriminator)#discriminator = MaxPooling2D(pool_size=(2, 2))(discriminator)
- # Flatten
- discriminator = Flatten()(discriminator)
- # Dense Layer
- discriminator = Dense(1)(discriminator)
- # Sigmoid Activation
- discriminator = Activation('sigmoid')(discriminator)
- # Optimizer and Compiling model
- dis_opt = Adam(lr=0.0002, beta_1=0.5)
- discriminator_model = Model(input = dis_input, output = discriminator)
- discriminator_model.compile(loss='binary_crossentropy', optimizer=dis_opt, metrics=['accuracy'])
- discriminator_model.summary()
- return discriminator_model
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