e0a649263bdc4b9803c4103f288a0675ebbecd5d,keras_applications/resnet_common.py,,block3,#Any#Any#Any#Any#Any#Any#Any#,194

Before Change

    x = layers.DepthwiseConv2D(kernel_size, strides=stride, depth_multiplier=c,
                               use_bias=False, name=name + "_2_conv")(x)
    x_shape = backend.int_shape(x)[1:-1]
    x = layers.Reshape(x_shape + (groups, c, c))(x)
    output_shape = x_shape + (groups, c) if backend.backend() == "theano" else None
    x = layers.Lambda(lambda x: sum([x[:, :, :, :, i] for i in range(c)]),
                      output_shape=output_shape, name=name + "_2_reduce")(x)
    x = layers.Reshape(x_shape + (filters,))(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + "_2_bn")(x)
    x = layers.Activation("relu", name=name + "_2_relu")(x)

After Change

    for i in range(filters):
        start = (i // c) * c * c + i % c
        end = start + c * c
        kernel[:, :, start:end:c, i] = 1.
    x = layers.Conv2D(filters, 1, use_bias=False, trainable=False,
                      kernel_initializer={"class_name": "Constant",
                                          "config": {"value": kernel}},

Instances