问Tensorflow : LSTM模型中的形状错误应为shape=(None，None，90)，找到shape=[90，1，78]

EN

def music_inference_model(LSTM_cell, densor, Ty=100):
    """
    Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.
    
    Arguments:
    LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
    densor -- the trained "densor" from model(), Keras layer object
    Ty -- integer, number of time steps to generate
    
    Returns:
    inference_model -- Keras model instance
    """
    
    # Get the shape of input values
    n_values = densor.units
    # Get the number of the hidden state vector
    n_a = LSTM_cell.units
    
    # Define the input of your model with a shape 
    x0 = Input(shape=(1, n_values))
    
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    x = x0

    ### START CODE HERE ###
    # Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
    outputs = []
    
    # Step 2: Loop over Ty and generate a value at every time step
    for t in range(Ty):
        # Step 2.A: Perform one step of LSTM_cell (≈1 line)
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        
        # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
        out = densor(_)
        # Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 90) (≈1 line)
        outputs.append(out)
 
        # Step 2.D: 
        # Select the next value according to "out",
        # Set "x" to be the one-hot representation of the selected value
        # See instructions above.
        x = tf.math.argmax(out)
        x = tf.one_hot(indices=x, depth=78) 
        # Step 2.E: 
        # Use RepeatVector(1) to convert x into a tensor with shape=(None, 1, 90)
        x = RepeatVector(1)(x)
        
    # Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
    inference_model = Model(inputs=[x0, a0, c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return inference_model

inference_model = music_inference_model(LSTM_cell, densor, Ty = 50)

ValueError                                Traceback (most recent call last)
<ipython-input-19-a33998d93c7b> in <module>
----> 1 inference_model = music_inference_model(LSTM_cell, densor, Ty = 50)

<ipython-input-18-ead9bae0b252> in music_inference_model(LSTM_cell, densor, Ty)
     38     for t in range(Ty):
     39         # Step 2.A: Perform one step of LSTM_cell (≈1 line)
---> 40         a, _, c = LSTM_cell(x, initial_state=[a, c])
     41 
     42         # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    707       # Perform the call with temporarily replaced input_spec
    708       self.input_spec = full_input_spec
--> 709       output = super(RNN, self).__call__(full_input, **kwargs)
    710       # Remove the additional_specs from input spec and keep the rest. It is
    711       # important to keep since the input spec was populated by build(), and

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    924     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    925       return self._functional_construction_call(inputs, args, kwargs,
--> 926                                                 input_list)
    927 
    928     # Maintains info about the `Layer.call` stack.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1090       # TODO(reedwm): We should assert input compatibility after the inputs
   1091       # are casted, not before.
-> 1092       input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
   1093       graph = backend.get_graph()
   1094       # Use `self._name_scope()` to avoid auto-incrementing the name.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    225                                ' is incompatible with layer ' + layer_name +
    226                                ': expected shape=' + str(spec.shape) +
--> 227                                ', found shape=' + str(shape))
    228 
    229 

ValueError: Input 0 is incompatible with layer lstm: expected shape=(None, None, 90), found shape=[90, 1, 78]