def k_autoencoder4(latent_dim, folder):
inputs = kst.md_input((32, 32, 3))
net = kst.conv2d(inputs, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.conv2d(net, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.maxpool2d(net) # 16,16,64
net = kst.conv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.conv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.maxpool2d(net) # 8,8,128
net = kst.conv2d(net, 256, (3, 3)) # 8,8,256
net = kst.activation(net, "relu")
net = kst.conv2d(net, 256, (3, 3))
net = kst.flatten(net) # 8*8*256
net = kst.dense(net, 2048) # 1024
net = kst.activation(net, "relu")
net = kst.dense(net, latent_dim)
encode = kst.activation(net, "relu")
net = kst.dense(encode, 2048) #19
net = kst.activation(net, "relu")
net = kst.dense(net, 8 * 8 * 256)
net = kst.activation(net, "relu")
net = kst.reshape(net, (8, 8, 256)) # 4,4,512
# net = kst.deconv2d(net, 512, (3, 3))
# net = kst.activation(net, "relu")
# net = kst.deconv2d(net, 512, (3, 3))
# net = kst.upsampling(net, 2) # 8,8,512
net = kst.deconv2d(net, 256, (3, 3)) # 8,8,256
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 256, (3, 3))
net = kst.upsampling(net, 2) # 16,16,256
net = kst.deconv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 128, (3, 3))
net = kst.activation(net, "relu")
net = kst.upsampling(net, 2) # 32,32,128
net = kst.deconv2d(net, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 64, (3, 3))
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 3, (3, 3)) # 32,32,3
decode = kst.activation(net, "sigmoid")
auto_encoder = Model(inputs, decode)
net_struct = kst.model_summary(auto_encoder,
print_out=True,
save_dir=folder + "/model_summary.txt")
encoder = Model(inputs, encode)
encoded_input = kst.md_input(shape=(latent_dim, ))
decoding = auto_encoder.layers[19](encoded_input)
for layer in auto_encoder.layers[20:]:
decoding = layer(decoding)
decoder = Model(encoded_input, decoding)
return auto_encoder, encoder, decoder
validation_split=validate_rate
)
train_generator = datagen.flow(
x_train, y_train,
batch_size=bt_size,
subset='training'
)
validate_generator = datagen.flow(
x_train, y_train,
batch_size=bt_size,
subset='validation'
)
K.clear_session()
inputs = kst.md_input(shape=(48, 48, 1))
net = kst.conv2d(inputs, 64, (3, 3))
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.conv2d(net, 64, (3, 3))
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.maxpool2d(net)
net = kst.conv2d(net, 128, (3, 3))
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.conv2d(net, 128, (3, 3))
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.maxpool2d(net)
net = kst.conv2d(net, 256, (3, 3))
net = kst.batch_norm(net)
w_init = ['he_normal', 'he_normal', 'he_normal']
param = {}
param['Epochs'] = epochs
param['Validate_Rate'] = validate_rate
param['Batch_size'] = bt_size
param['Learning_Rate'] = learn_rate
param['Weight_initializer'] = w_init[0] + " " + w_init[1]
param['Bias_initializer'] = b_init[0] + " " + str(b_init[1])
############################################################
# Build model:
############################################################
K.clear_session()
inputs = kst.md_input(shape=img_shape)
net = kst.conv2d(inputs, 64, (3, 3), kernel_init=w_init[2], bias_init=b_init[1])
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net)
net = kst.conv2d(net, 128, (3, 3), kernel_init=w_init[2], bias_init=b_init[1])
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net)
net = kst.conv2d(net, 256, (3, 3), kernel_init=w_init[2], bias_init=b_init[1])
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net)
net = kst.flatten(net)
net = kst.dense(net, 512, kernel_init=w_init[2], bias_init=b_init[1])
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
def k_inception_v3(n_class, dp):
# TODO: make sure the reliability"
inputs = kst.md_input((299, 299, 1))
# inputs = K.resize_images(inputs, 299, 299, data_format="channels_last")
net = kst.conv2d(inputs, 32, (3, 3), stride=2, padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 32, (3, 3), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 64, (3, 3), padding="SAME")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net, kernel_size=(3, 3), stride=2, padding="VALID")
net = kst.conv2d(net, 80, (1, 1), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 192, (3, 3), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net, (3, 3), padding="VALID")
# mixed 5b:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 32, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 5c:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 64, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 5d:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 64, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6a:
branch0 = kst.conv2d(net, 348, (3, 3), stride=2, padding="VALID")
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 64, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 96, (3, 3))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 96, (3, 3), stride=2, padding="VALID")
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.maxpool2d(net, (3, 3), padding="VALID")
net = kst.concat([branch0, branch1, branch2], axis=3)
# mixed 6b:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 128, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 128, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 128, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6c:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 160, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 160, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 160, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6d:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 160, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 160, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 160, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6e:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 192, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 192, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
aux_logits = net
# mixed 7a:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch0 = kst.conv2d(branch0, 320, (3, 3), stride=2, padding="VALID")
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 192, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (3, 3), stride=2, padding="VALID")
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.maxpool2d(net, (3, 3), padding="VALID")
net = kst.concat([branch0, branch1, branch2], axis=3)
# mixed 7b:
branch0 = kst.conv2d(net, 320, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 384, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1a = kst.conv2d(branch1, 384, (1, 3))
branch1a = kst.batch_norm(branch1a)
branch1a = kst.activation(branch1a, "relu")
branch1b = kst.conv2d(branch1, 384, (3, 1))
branch1b = kst.batch_norm(branch1b)
branch1b = kst.activation(branch1b, "relu")
branch1 = kst.concat([branch1a, branch1b], axis=3)
branch2 = kst.conv2d(net, 448, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2 = kst.conv2d(branch2, 384, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2a = kst.conv2d(branch2, 384, (1, 3))
branch2a = kst.batch_norm(branch2a)
branch2a = kst.activation(branch2a, "relu")
branch2b = kst.conv2d(branch1, 384, (3, 1))
branch2b = kst.batch_norm(branch2b)
branch2b = kst.activation(branch2b, "relu")
branch2 = kst.concat([branch2a, branch2b], axis=3)
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 7c:
branch0 = kst.conv2d(net, 320, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 384, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1a = kst.conv2d(branch1, 384, (1, 3))
branch1a = kst.batch_norm(branch1a)
branch1a = kst.activation(branch1a, "relu")
branch1b = kst.conv2d(branch1, 384, (3, 1))
branch1b = kst.batch_norm(branch1b)
branch1b = kst.activation(branch1b, "relu")
branch1 = kst.concat([branch1a, branch1b], axis=3)
branch2 = kst.conv2d(net, 448, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2 = kst.conv2d(branch2, 384, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2a = kst.conv2d(branch2, 384, (1, 3))
branch2a = kst.batch_norm(branch2a)
branch2a = kst.activation(branch2a, "relu")
branch2b = kst.conv2d(branch1, 384, (3, 1))
branch2b = kst.batch_norm(branch2b)
branch2b = kst.activation(branch2b, "relu")
branch2 = kst.concat([branch2a, branch2b], axis=3)
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# auxlogits:
aux_logits = kst.avgpool2d(aux_logits, (5, 5), stride=3, padding="VALID")
aux_logits = kst.conv2d(aux_logits, 128, (1, 1))
aux_logits = kst.batch_norm(aux_logits)
aux_logits = kst.activation(aux_logits, "relu")
aux_logits = kst.conv2d(aux_logits, 768, (5, 5), padding="VALID")
aux_logits = kst.batch_norm(aux_logits)
aux_logits = kst.activation(aux_logits, "relu")
aux_logits = kst.conv2d(aux_logits, n_class, (1, 1))
aux_logits = kst.flatten(aux_logits)
# aux_logits = kst.squeeze(aux_logits, 1)
# aux_logits = kst.squeeze(aux_logits, 1)
aux_predict = kst.activation(aux_logits, "softmax")
# Logits:
net = kst.avgpool2d(net, (8, 8), padding="VALID")
net = kst.dropout(net, dp)
logits = kst.conv2d(net, n_class, (1, 1))
# logits = kst.squeeze(logits, 1)
# logits = kst.squeeze(logits, 1)
logits = kst.flatten(logits)
predict = kst.activation(logits, "softmax")
inception_v3 = Model(inputs, [predict, aux_predict])
net_struct = kst.model_summary(inception_v3, print_out=True)
# aux_inception = Model(inputs, aux_predict)
return inception_v3