Define a model

TensorLayer provides two ways to define a model. Static model allows you to build model in a fluent way while dynamic model allows you to fully control the forward process.

Static model

import tensorflow as tf
from tensorlayer.layers import Input, Dropout, Dense
from tensorlayer.models import Model

def get_model(inputs_shape):
    ni = Input(inputs_shape)
    nn = Dropout(keep=0.8)(ni)
    nn = Dense(n_units=800, act=tf.nn.relu, name="dense1")(nn)
    nn = Dropout(keep=0.8)(nn)
    nn = Dense(n_units=800, act=tf.nn.relu)(nn)
    nn = Dropout(keep=0.8)(nn)
    nn = Dense(n_units=10, act=tf.nn.relu)(nn)
    M = Model(inputs=ni, outputs=nn, name="mlp")
    return M

MLP = get_model([None, 784])
MLP.eval()
outputs = MLP(data)

Dynamic model

In this case, you need to manually input the output shape of the previous layer to the new layer.

class CustomModel(Model):

    def __init__(self):
        super(CustomModel, self).__init__()

        self.dropout1 = Dropout(keep=0.8)
        self.dense1 = Dense(n_units=800, act=tf.nn.relu, in_channels=784)
        self.dropout2 = Dropout(keep=0.8)#(self.dense1)
        self.dense2 = Dense(n_units=800, act=tf.nn.relu, in_channels=800)
        self.dropout3 = Dropout(keep=0.8)#(self.dense2)
        self.dense3 = Dense(n_units=10, act=tf.nn.relu, in_channels=800)

    def forward(self, x, foo=False):
        z = self.dropout1(x)
        z = self.dense1(z)
        z = self.dropout2(z)
        z = self.dense2(z)
        z = self.dropout3(z)
        out = self.dense3(z)
        if foo:
            out = tf.nn.relu(out)
        return out

MLP = CustomModel()
MLP.eval()
outputs = MLP(data, foo=True) # controls the forward here
outputs = MLP(data, foo=False)

Switching train/test modes

# method 1: switch before forward
Model.train() # enable dropout, batch norm moving avg ...
output = Model(train_data)
... # training code here
Model.eval()  # disable dropout, batch norm moving avg ...
output = Model(test_data)
... # testing code here

# method 2: switch while forward
output = Model(train_data, is_train=True)
output = Model(test_data, is_train=False)

Reuse weights

For static model, call the layer multiple time in model creation

# create siamese network

def create_base_network(input_shape):
      '''Base network to be shared (eq. to feature extraction).
      '''
      input = Input(shape=input_shape)
      x = Flatten()(input)
      x = Dense(128, act=tf.nn.relu)(x)
      x = Dropout(0.9)(x)
      x = Dense(128, act=tf.nn.relu)(x)
      x = Dropout(0.9)(x)
      x = Dense(128, act=tf.nn.relu)(x)
      return Model(input, x)


def get_siamese_network(input_shape):
      """Create siamese network with shared base network as layer
      """
      base_layer = create_base_network(input_shape).as_layer() # convert model as layer

      ni_1 = Input(input_shape)
      ni_2 = Input(input_shape)
      nn_1 = base_layer(ni_1) # call base_layer twice
      nn_2 = base_layer(ni_2)
      return Model(inputs=[ni_1, ni_2], outputs=[nn_1, nn_2])

siamese_net = get_siamese_network([None, 784])

For dynamic model, call the layer multiple time in forward function

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.dense_shared = Dense(n_units=800, act=tf.nn.relu, in_channels=784)
        self.dense1 = Dense(n_units=10, act=tf.nn.relu, in_channels=800)
        self.dense2 = Dense(n_units=10, act=tf.nn.relu, in_channels=800)
        self.cat = Concat()

    def forward(self, x):
        x1 = self.dense_shared(x) # call dense_shared twice
        x2 = self.dense_shared(x)
        x1 = self.dense1(x1)
        x2 = self.dense2(x2)
        out = self.cat([x1, x2])
        return out

model = MyModel()

Get specific weights

We can get the specific weights by indexing or naming.

# indexing
all_weights = MLP.all_weights
some_weights = MLP.all_weights[1:3]

# naming
some_weights = MLP.get_layer('dense1').all_weights

Save and restore model

We provide two ways to save and restore models

Save weights only

MLP.save_weights('model_weights.h5') # by default, file will be in hdf5 format
MLP.load_weights('model_weights.h5')

Save model architecture and weights (optional)

# When using Model.load(), there is no need to reimplement or declare the architecture of the model explicitly in code
MLP.save('model.h5', save_weights=True)
MLP = Model.load('model.h5', load_weights=True)