def multi_input_model_generator():
util.set_tf_random_seed()
input0 = keras.Input(shape=(
28,
28,
1,
), name='input0')
input1 = keras.Input(shape=(
26,
26,
32,
), name='input1')
x = keras.layers.Conv2D(32,
3,
strides=(1, 1),
padding='valid',
name='conv')(input0)
y = tnt_layers.SplitLayer(name="split_layer0")(x)
z = tnt_layers.SplitLayer(name="split_layer1")(x)
x = keras.layers.Conv2D(32,
1,
strides=(1, 1),
padding='valid',
name='conv2')(y)
x = tnt_layers.SplitLayer(name="split_layer2")(x)
x = keras.layers.Concatenate(name='concat')([input1, x, z])
x = keras.layers.Flatten(name='flatten')(x)
outputs = keras.layers.Dense(10,
activation='softmax',
name='dense_softmax')(x)
model = keras.Model(inputs=[input0, input1], outputs=outputs)
return model
def skip_connection_model_generator():
util.set_tf_random_seed()
inputs = keras.Input(shape=(
28,
28,
1,
), name='input')
x = keras.layers.Conv2D(32,
3,
strides=(1, 1),
padding='valid',
name='conv')(inputs)
y = tnt_layers.SplitLayer(name="split_layer0")(x)
z = tnt_layers.SplitLayer(name="split_layer1")(x)
x = keras.layers.Conv2D(32,
1,
strides=(1, 1),
padding='valid',
activation='relu',
name='conv_relu')(y)
x = tnt_layers.SplitLayer(name="split_layer2")(x)
x = keras.layers.Concatenate(name='concat')([x, z])
x = keras.layers.Flatten(name='flatten')(x)
outputs = keras.layers.Dense(10,
activation='softmax',
name='dense_softmax')(x)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
def alexnet_model_generator():
util.set_tf_random_seed()
inputs = keras.Input(shape=(
28,
28,
1,
), name='input')
x = keras.layers.Conv2D(32, 3, strides=(1, 1), name='conv')(inputs)
x = keras.layers.MaxPooling2D(pool_size=(3, 3),
strides=(1, 1),
name='maxpool')(x)
x = tnt_layers.SplitLayer(name="split_layer0")(x)
x = keras.layers.Conv2D(32, 3, strides=(1, 1), name='conv_two')(x)
x = keras.layers.MaxPooling2D(pool_size=(3, 3),
strides=(1, 1),
name='maxpool_two')(x)
x = tnt_layers.SplitLayer(name="split_layer1")(x)
x = keras.layers.Conv2D(64, 3, strides=(1, 1), name='conv_three')(x)
x = keras.layers.MaxPooling2D(pool_size=(3, 3),
strides=(2, 2),
name='maxpool_three')(x)
x = keras.layers.Flatten(name='flatten')(x)
x = keras.layers.Dense(512, activation='relu', name='dense_relu')(x)
outputs = keras.layers.Dense(10,
activation='softmax',
name='dense_softmax')(x)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
def simple_model_generator(): util.set_tf_random_seed() input0 = keras.Input(shape=(28,28,1,), name='input') x = keras.layers.Flatten()(input0) x = keras.layers.Dense(2, activation='relu')(x) x = tnt_layers.SplitLayer(name="split_layer1")(x) output = keras.layers.Dense(10, activation='softmax', name='dense_softmax')(x) model = keras.Model(inputs=input0, outputs=output) return model
def multi_output_model_generator():
util.set_tf_random_seed()
input0 = keras.Input(shape=(
28,
28,
1,
), name='input')
x = keras.layers.Flatten(name='flatten')(input0)
x = keras.layers.Dense(10, activation='relu', name='dense_relu')(x)
y = tnt_layers.SplitLayer(name="ten_classes")(x)
z = tnt_layers.SplitLayer(name="two_classes")(x)
x = keras.layers.Add(name='add')([y, z])
output0 = keras.layers.Dense(10, activation='relu',
name='dense_softmax10')(x)
output1 = keras.layers.Dense(2,
activation='softmax',
name='dense_softmax2')(x)
model = keras.Model(inputs=input0,
outputs=[output0, output1],
name="model")
return model
def fc_model_generator():
util.set_tf_random_seed()
reference_input = keras.Input(shape=(
28,
28,
1,
), name='reference_input')
reference_x = keras.layers.Flatten(name='flatten')(reference_input)
reference_x = keras.layers.Dense(10, activation='relu',
name='dense_relu')(reference_x)
reference_x = tnt_layers.SplitLayer(name="split_layer1")(reference_x)
reference_output = keras.layers.Dense(10,
activation='softmax',
name='dense_softmax')(reference_x)
reference_model = keras.Model(inputs=reference_input,
outputs=reference_output,
name="reference_model")
return reference_model
def incorrect_split_model():
inputs = keras.Input(shape=(
28,
28,
1,
), name='input')
x = keras.layers.Conv2D(32,
3,
strides=(1, 1),
padding='valid',
name='conv')(inputs)
y = tnt_layers.SplitLayer(name="split_layer0")(x)
z = keras.layers.Conv2D(32,
1,
strides=(1, 1),
padding='valid',
activation='relu',
name='conv_relu')(y)
x = keras.layers.Concatenate(name='concat')([x, z])
model = keras.Model(inputs=inputs, outputs=x)
return model