6cb84f4b742861e823015cac18535efa468b0d81,autokeras/layer_transformer.py,,conv_dense_to_wider_layer,#Any#Any#Any#,128

Before Change

    pre_filter_shape = pre_layer.kernel_size
    conv_func = get_conv_layer_func(len(pre_filter_shape))
    n_pre_filters = pre_layer.filters
    n_units = next_layer.get_weights().shape[1]

    teacher_w1 = pre_layer.get_weights()[0]
    teacher_w2 = next_layer.get_weights()[0]
    n_total_weights = reduce(mul, teacher_w2.shape)

After Change

    new_pre_layer.build((None,) * (len(pre_filter_shape) + 1) + (pre_layer.input_shape[-1],))
    new_pre_layer.set_weights((student_w1, student_b1))
    new_next_layer = Dense(n_units, activation="relu")
    n_new_total_weights = int(reduce(mul, student_w2.shape))
    input_dim = int(n_new_total_weights / n_units)
    new_next_layer.build((None, input_dim))
    new_next_layer.set_weights((student_w2.reshape(input_dim, n_units), teacher_b2))
    return new_pre_layer, new_next_layer

Instances