How to fourth layer to tensor to let convolution consume it-CodePudding

I am trying to add preprocessing layers to a tensorflow model but can't figure out how to use tf.keras.layers.Reshape correctly.

I have some previous preprocessing layers that bring a numpy image to a tensorflow tensor of shape TensorShape([50, 50, 3]).

The first layer of the model that I am trying to connect these preprocessing layers to is a convolutional layer which requires a four dimensional input. When I call the model on a tensor of dimensions Tensorshape([50,50,3]), with three dimensions, I get the error:

Input 0 of layer "Conv1" is incompatible with the layer: expected min_ndim=4, found ndim=3. 
Full shape received: (50, 50, 3)

Call arguments received by layer "model_1" (type Functional):
  • inputs=tf.Tensor(shape=(50, 50, 3), dtype=float32)
  • training=False
  • mask=None

Changing the input tensor to a numpy array and sizing it to a (1,50,50,3) array, then inputting that into the model works fine. I want to do this with a tensorflow layer however so that I can save the preprocessing and model inference into a single Saved Model format tensorflow file without having to do python preprocessing.

tf.expand_dims(input_tensor, axis=0) works, but it's not a layer object so I can't use it. It looks like tf.keras.layers.Reshape((1,50,50,3), input_shape=(50,50,3)) or tf.keras.layers.Reshape((1,50,50,3)) is the way to go then.

My silly problem is that I just can't figure out how to use tf.keras.layers.Reshape, even using the tf/keras documentation. When I pass in a tensor of dimensions TensorShape([50, 50, 3]), to tf.keras.layers.Reshape((1,50,50,3)) I get the error message:

Input to reshape is a tensor with 7500 values, but the requested shape has 375000 [Op:Reshape]

Call arguments received by layer "reshape_9" (type Reshape):
  • inputs=tf.Tensor(shape=(50, 50, 3), dtype=float32)

So, how do I get this to work? All I want is to end up with the same Tensor as inputted but with TensorShape([1,50,50,3]) instead of TensorShape([50,50,3]), and I want this to happen with a tf.keras.layers object. So really I just want to perform an identity transformation that adds a fourth dimension of size one at the beginning of a tensor so that a convolutional layer can consume it, and I want this from a tf.keras.layers object.

CodePudding user response：

You need to make sure to add an extra dimension for the batch size, if you are passing in a single image the batch size would be 1. You can use np.expand_dims to add the extra dimension.

CodePudding user response：

It's because the images are put together into batches. Try

tf.keras.layers.Reshape((-1,50,50,3))

Or you can use a Lambda layer instead:

tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1))

The value -1 means the reshape or expand_dims function will calculate that shape dimension for you, depending on the number of values it receives.