Q:

Is there a method in tensorflow.keras.layers to get the output shape of a specific layer before running the model?

I am building a CNN with Keras using a TensorFlow backend with the following structure:
# Create the Second Model in Ensemble
    def createModel(self, model_input, n_outputs, first_session=True):
        
        if first_session != True:
            model = load_model('ideal_model.hdf5')
            
            return model
        
        # Define Input Layer
        inputs = model_input
    
        # Define Max Pooling Layer
        conv = MaxPooling2D(pool_size=(3, 3), padding='same')(inputs)
        
        # Define Layer Normalization Layer
        conv = LayerNormalization()(inputs)
    
        # Define Leaky ReLU Layer
        conv = LeakyReLU(alpha=0.1)(conv)
    
        # Define Dropout Layer
        conv = Dropout(0.2)(conv)
    
        # Define First Conv2D Layer
        conv = Conv2D(filters=64,
                      kernel_size=(3, 3),
                      activation='relu',
                      padding='same',
                      strides=(3, 2))(conv)
        conv = Dropout(0.3)(conv)
    
        # Define Second Conv2D Layer
        conv = Conv2D(filters=32,
                      kernel_size=(5, 5),
                      activation='relu',
                      padding='same',
                      strides=(3, 2))(conv)
        conv = Dropout(0.3)(conv)
    
        # Define Softmax Layer
        conv = Softmax(axis=1)(conv)

        # Define Reshape Layer
        conv = Reshape((conv._keras_shape[1]*conv._keras_shape[2]*conv._keras_shape[3],))(conv)
    
        # Define Sigmoid Dense Layer
        conv = Dense(64, activation='sigmoid')(conv)
    
        # Define Output Layer
        outputs = Dense(n_outputs, activation='softmax')(conv)
    
        # Create Model
        model = Model(inputs, outputs)
        
        model.summary()
        
        return model

Currently, I am running into a bit of trouble since I am trying to use a Reshape layer to flatten the tensor, and I am trying to avoid hard-coding the dimensions of the output from the previous layer into the Reshape layer, if possible. (Note: Flatten layers are not supported by the kernels in the FPGA on which the program will ultimately run, so I cannot use them.) The above code produces the following error:
AttributeError: 'Tensor' object has no attribute '_keras_shape'

This occurs because I had to import the layers using tensorflow.keras.layers (as opposed to keras.layers) due to the LayerNormalization layer at the beginning of the model architecture.
So, I was wondering if there is a method to get the output shape of a specific layer in tensorflow.keras.layers before compiling the model.

A:

conv.shape or maybe tf.shape(conv)

