def test_rebuild_model():
    model = Sequential()
    model.add(Dense(128, input_shape=(784,)))
    model.add(Dense(64))
    assert(model.get_layer(index=-1).output_shape == (None, 64))

    model.add(Dense(32))
    assert(model.get_layer(index=-1).output_shape == (None, 32))
Example #2
0
def make_wider_student_model(teacher_model, train_data,
                             validation_data, init, epochs=3):
    '''Train a wider student model based on teacher_model,
       with either 'random-pad' (baseline) or 'net2wider'
    '''
    new_conv1_width = 128
    new_fc1_width = 128

    model = Sequential()
    # a wider conv1 compared to teacher_model
    model.add(Conv2D(new_conv1_width, 3, input_shape=input_shape,
                     padding='same', name='conv1'))
    model.add(MaxPooling2D(2, name='pool1'))
    model.add(Conv2D(64, 3, padding='same', name='conv2'))
    model.add(MaxPooling2D(2, name='pool2'))
    model.add(Flatten(name='flatten'))
    # a wider fc1 compared to teacher model
    model.add(Dense(new_fc1_width, activation='relu', name='fc1'))
    model.add(Dense(num_class, activation='softmax', name='fc2'))

    # The weights for other layers need to be copied from teacher_model
    # to student_model, except for widened layers
    # and their immediate downstreams, which will be initialized separately.
    # For this example there are no other layers that need to be copied.

    w_conv1, b_conv1 = teacher_model.get_layer('conv1').get_weights()
    w_conv2, b_conv2 = teacher_model.get_layer('conv2').get_weights()
    new_w_conv1, new_b_conv1, new_w_conv2 = wider2net_conv2d(
        w_conv1, b_conv1, w_conv2, new_conv1_width, init)
    model.get_layer('conv1').set_weights([new_w_conv1, new_b_conv1])
    model.get_layer('conv2').set_weights([new_w_conv2, b_conv2])

    w_fc1, b_fc1 = teacher_model.get_layer('fc1').get_weights()
    w_fc2, b_fc2 = teacher_model.get_layer('fc2').get_weights()
    new_w_fc1, new_b_fc1, new_w_fc2 = wider2net_fc(
        w_fc1, b_fc1, w_fc2, new_fc1_width, init)
    model.get_layer('fc1').set_weights([new_w_fc1, new_b_fc1])
    model.get_layer('fc2').set_weights([new_w_fc2, b_fc2])

    model.compile(loss='categorical_crossentropy',
                  optimizer=SGD(lr=0.001, momentum=0.9),
                  metrics=['accuracy'])

    train_x, train_y = train_data
    history = model.fit(train_x, train_y,
                        epochs=epochs,
                        validation_data=validation_data)
    return model, history
Example #3
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def test_get_layer():
    model = Sequential()
    model.add(Dense(1, input_dim=2))
    with pytest.raises(ValueError):
        model.get_layer(index=5)
    with pytest.raises(ValueError):
        model.get_layer(index=None)
    with pytest.raises(ValueError):
        model.get_layer(name='conv')
Example #4
0
def make_deeper_student_model(teacher_model, train_data,
                              validation_data, init, epochs=3):
    '''Train a deeper student model based on teacher_model,
       with either 'random-init' (baseline) or 'net2deeper'
    '''
    model = Sequential()
    model.add(Conv2D(64, 3, input_shape=input_shape,
                     padding='same', name='conv1'))
    model.add(MaxPooling2D(2, name='pool1'))
    model.add(Conv2D(64, 3, padding='same', name='conv2'))
    # add another conv2d layer to make original conv2 deeper
    if init == 'net2deeper':
        prev_w, _ = model.get_layer('conv2').get_weights()
        new_weights = deeper2net_conv2d(prev_w)
        model.add(Conv2D(64, 3, padding='same',
                         name='conv2-deeper', weights=new_weights))
    elif init == 'random-init':
        model.add(Conv2D(64, 3, padding='same', name='conv2-deeper'))
    else:
        raise ValueError('Unsupported weight initializer: %s' % init)
    model.add(MaxPooling2D(2, name='pool2'))
    model.add(Flatten(name='flatten'))
    model.add(Dense(64, activation='relu', name='fc1'))
    # add another fc layer to make original fc1 deeper
    if init == 'net2deeper':
        # net2deeper for fc layer with relu, is just an identity initializer
        model.add(Dense(64, kernel_initializer='identity',
                        activation='relu', name='fc1-deeper'))
    elif init == 'random-init':
        model.add(Dense(64, activation='relu', name='fc1-deeper'))
    else:
        raise ValueError('Unsupported weight initializer: %s' % init)
    model.add(Dense(num_class, activation='softmax', name='fc2'))

    # copy weights for other layers
    copy_weights(teacher_model, model, layer_names=[
                 'conv1', 'conv2', 'fc1', 'fc2'])

    model.compile(loss='categorical_crossentropy',
                  optimizer=SGD(lr=0.001, momentum=0.9),
                  metrics=['accuracy'])

    train_x, train_y = train_data
    history = model.fit(train_x, train_y,
                        epochs=epochs,
                        validation_data=validation_data)
    return model, history