"InvalidArgumentError: Incompatible shapes: [64,40000] vs. [64] [Op:Mul]" while doing operations between tensors?

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I'm trying to do this operation between two tensors:

green_mat = sio.loadmat('green.mat')
green = np.array(green_mat['G2'])
green = tf.convert_to_tensor(green)
green = tf.cast(green, dtype='complex64')  # >>>green.shape = TensorShape([64, 40000])



tensor = tf.ones(128,1)        # tensor.shape = TensorShape([128])

def mul_and_sum(tensor):
   real = tensor[0:64]
   imag = tensor[64:128]
   complex_tensor = tf.complex(real, imag)
   return tf.reduce_sum((tf.multiply(green, complex_tensor), 1))

res = mul_and_sum(tensor)

basically, at the end what I want to obtain is a tensor with 40000 elements to use as a layer for a neural network, but when I run this function as a test I have this error:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [64,40000] vs. [64] [Op:Mul]

It's the first time I'm working on tensors operation and maybe I'm a bit confused on how to treat the dimensions, any suggestions? Thanks :)

EDIT: ok, I have understood the point, indeed for the example that I provided everything works, but then I have another problem in my network:

def convolution(tensor):
tf.cast(tensor, dtype='float64')
real = tensor[0:64]
imag = tensor[64:128]
complex_tensor = tf.complex(real, imag)
a = tf.math.real(tf.reduce_sum((tf.multiply(green, complex_tensor)), 0))
return a

def get_model3(mask_kind):
epochs = 200
learning_rate = 0.1
decay_rate = learning_rate / epochs
inp_1 = keras.Input(shape=(64, 101, 129), name="RST_inputs")
x = layers.Conv2D(1, kernel_size=(1, 1), strides=(1, 1), padding="valid", trainable=False)(inp_1)
x = layers.Conv2D(256, kernel_size=(3, 3), kernel_regularizer=l2(1e-6), strides=(3, 3), padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(128, kernel_size=(3, 3), kernel_regularizer=l2(1e-6), strides=(3, 3), padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(64, kernel_size=(2, 2), kernel_regularizer=l2(1e-6), strides=(2, 2), padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(32, kernel_size=(2, 2), kernel_regularizer=l2(1e-6), strides=(2, 2), padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Flatten()(x)
x = layers.Dense(512)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Dense(256)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
out1 = layers.Dense(128, name="ls_weights")(x)

if mask_kind == 1:
    binary_mask = layers.Lambda(mask_layer1, name="lambda_layer", dtype='float64')(out1)
elif mask_kind == 2:
    binary_mask = layers.Lambda(mask_layer2, name="lambda_layer", dtype='float64')(out1)
else:
    binary_mask = out1
#here the binary mask shape is [?,128]
binary_mask = tf.expand_dims(binary_mask, axis=2)  #here the shape is [?,128,1]
binary_mask = tf.squeeze(binary_mask, axis=0) #here the shape is [128,1]

print('binary shape:', binary_mask.shape)

lambda_layer = layers.Lambda(convolution, name="convolutional_layer")(binary_mask)
print(lambda_layer.shape)


model3 = keras.Model(inp_1,  lambda_layer, name="2_out_model")

model3.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=decay_rate),  # in caso
               # rimettere 0.001
               loss="mean_squared_error")

plot_model(model3, to_file='model.png', show_shapes=True, show_layer_names=True)
model3.summary()

return model3

and I get this error:

ValueError: Input 0 of layer sf_vec is incompatible with the layer: : expected min_ndim=2, found ndim=1. Full shape received: [40000]

I know that it is because there is a mismatch between the dimension, but the fact is that the shape of the output layer(tensor) should be [?,40000] and instead I get only a tensor of [40000], any suggestion?

EDIT 2.0 I didn't noticed that my output was already the lambda layer so in the way the model is written I get no errors, but from the summary in this way I get as lambda shape (1, 40000) while usually should be (None,40000). Where is the error?

tensor tensorflow layer neural-network python
1 Answers

If you want to do a multiplication between 2 tensors, they need to have compatible shape, i.e, either the same shape, or a shape that is broadcastable. Quoting the numpy documentation (tensorflow follows the same broadcasting rules):

When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions and works its way forward. Two dimensions are compatible when

  • they are equal, or
  • one of them is 1

In your case, if you want to use tf.multiply, you need to add a dimension to your vector so that it has a the same number of dimensions. You can do that by using tf.expand_dims or using advanced indexing with tf.newaxis.

An example (using complexes, like in your question):

>>> a = tf.complex(tf.random.normal((64,128)),tf.random.normal((64,128)))
>>> a.shape
TensorShape([64, 128])
>>> b = tf.complex(tf.ones(64),tf.ones(64))
>>> b.shape
TensorShape([64])

To be able to use tf.multiply, you need to add a dimension to b :

>>> b_exp = tf.exand_dims(b, axis=1)
>>> b_exp.shape
TensorShape([64, 1])
>>> tf.multiply(a,b_exp).shape
TensorShape([64, 128])

Note: doing tf.reduce_sum on tf.multiply can be akin to just do a matrix multiplication

In your case, you can probably do something similar to

>>> tf.matmul(b[tf.newaxis,:], a).shape
TensorShape([1, 128])

If the extra dimension bothers you, you can get rid of it with tf.squeeze.

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