Skip to content

Update CsiNet_train.py #13

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
+21 −18
Open

Update CsiNet_train.py #13

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
39 changes: 21 additions & 18 deletions CsiNet_train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,44 +19,47 @@
encoded_dim = 512 #compress rate=1/4->dim.=512, compress rate=1/16->dim.=128, compress rate=1/32->dim.=64, compress rate=1/64->dim.=32

# Bulid the autoencoder model of CsiNet
def residual_network(x, residual_num, encoded_dim):
def add_common_layers(y):
def residual_block_decoded():
conv1 = Conv2D(8, kernel_size=(3, 3), padding='same')
conv2 = Conv2D(16, kernel_size=(3, 3), padding='same')
conv3 = Conv2D(2, kernel_size=(3, 3), padding='same')
bn = BatchNormalization()

def block(x):
shortcut = x
y = conv1(x)
y = BatchNormalization()(y)
y = LeakyReLU()(y)
return y
def residual_block_decoded(y):
shortcut = y
y = Conv2D(8, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)
y = add_common_layers(y)

y = Conv2D(16, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)
y = add_common_layers(y)

y = Conv2D(2, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)

y = conv2(y)
y = BatchNormalization()(y)
y = LeakyReLU()(y)

y = conv3(y)
y = bn(y)

y = add([shortcut, y])
y = LeakyReLU()(y)

return y
return block

x = Conv2D(2, (3, 3), padding='same', data_format="channels_first")(x)
x = Conv2D(2, (3, 3), padding='same')(x)
x = add_common_layers(x)


x = Reshape((img_total,))(x)
encoded = Dense(encoded_dim, activation='linear')(x)

x = Dense(img_total, activation='linear')(encoded)
x = Reshape((img_channels, img_height, img_width,))(x)
x = Reshape((img_height, img_width, img_channels))(x)
for i in range(residual_num):
x = residual_block_decoded(x)

x = Conv2D(2, (3, 3), activation='sigmoid', padding='same', data_format="channels_first")(x)
x = Conv2D(2, (3, 3), activation='sigmoid', padding='same')(x)

return x

image_tensor = Input(shape=(img_channels, img_height, img_width))
image_tensor = Input(shape=(img_height, img_width, img_channels))
network_output = residual_network(image_tensor, residual_num, encoded_dim)
autoencoder = Model(inputs=[image_tensor], outputs=[network_output])
autoencoder.compile(optimizer='adam', loss='mse')
Expand Down Expand Up @@ -200,4 +203,4 @@ def on_epoch_end(self, epoch, logs={}):
json_file.write(model_json)
# serialize weights to HDF5
outfile = "result/model_%s.h5"%file
autoencoder.save_weights(outfile)
autoencoder.save_weights(outfile)