3b7bb4e8caee69567bd83046f9a7370bd3081935,shared/gan.py,,discriminator_fast_densenet,#Any#Any#,372

Before Change

    result = batch_norm(config["batch_size"], name="d_expand_bn2a")(result)
    result = activation(result)
    result = conv2d(result, result.get_shape()[3], name="d_id", k_w=1, k_h=1, d_h=1, d_w=1)


    filter_size_w = int(result.get_shape()[1])
    filter_size_h = int(result.get_shape()[2])

After Change

    filter_size_w = int(result.get_shape()[1])
    filter_size_h = int(result.get_shape()[2])
    while filter_size_h > 1:
      result = dense_block(result, k, activation, batch_size, "transition", "d_layers_transition_"+str(i+10))
      filter_size_w = int(result.get_shape()[1])
      filter_size_h = int(result.get_shape()[2])
      print("densenet size", result)
      i+=1
    filter_size_w = int(result.get_shape()[1])
    filter_size_h = int(result.get_shape()[2])
    filter = [1,filter_size_w,filter_size_h,1]
    stride = [1,filter_size_w,filter_size_h,1]
    result = tf.nn.avg_pool(result, ksize=filter, strides=stride, padding="SAME")

Instances