could you give some examples about the shape in below?

[moses1994]

3. input_length (required for CTC loss)

    # this is the time dimension of CTC (batch x time x mfcc)
    #input_length = np.array([get_xsize(mfcc) for mfcc in X_data])
    input_length = np.array(x_val)
    # print("3. input_length shape:", input_length.shape)   
    # print("3. input_length =", input_length)
    assert(input_length.shape == (self.batch_size,))

    # 4. label_length (required for CTC loss)
    # this is the length of the number of label of a sequence
    #label_length = np.array([len(l) for l in labels])
    label_length = np.array(y_val)
    # print("4. label_length shape:", label_length.shape)
    # print("4. label_length =", label_length)
    assert(label_length.shape == (self.batch_size,))

hi, I want to make a ctc demo, I do not know the "label_length.shape" and "input_length.shape", how to calculate them ? and what means them ? thanks you.

[revive]

@moses1994 The shape is a member of numpy.array, which is a tuple representing the dimension of the array. shape of (2, 3) means a 2-dimentional matrix of 2x3. In this code, the label_length is an 1-dimensional array, and each element is the length of the transcript in the batch. So it's shape is (batch,). You don't need to calculate the shape of an array.