title: model.fit_generator : Cannot convert a symbolic Tensor (args_2:0) to a numpy array
body: <p>I an using tenserflow to build a model</p>
  <pre><code>inputs1 = Input(shape=(2048,))
  fe1 = Dropout(0.5)(inputs1)
  fe2 = Dense(256, activation='relu')(fe1)
  inputs2 = Input(shape=(max_length,))
  se1 = Embedding(vocab_size, embedding_dim, mask_zero=True)(inputs2)
  se2 = Dropout(0.5)(se1)
  se3 = LSTM(256)(se2)
  
  decoder1 =tf.keras.layers.Add()([fe2, se3])
  decoder2 = Dense(256, activation='relu')(decoder1)
  outputs = Dense(vocab_size, activation='softmax')(decoder2)
  model = Model(inputs=[inputs1, inputs2], outputs=outputs)
  </code></pre>
  <p>Then I am compiling it</p>
  <pre><code>model.compile(loss='categorical_crossentropy', optimizer='adam')
  </code></pre>
  <p>then I am using the fit</p>
  <pre><code>for i in range(epochs):
      generator = data_generator(train_descriptions, train_features, wordtoix, max_length, number_pics_per_bath)
      model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
  </code></pre>
  <p>The error that I get is :</p>
  <pre><code>C:\...../Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py:513 &lt;listcomp&gt;
      data = [np.asarray(d) for d in data]
  C:\.....\Anaconda3\lib\site-packages\numpy\core\_asarray.py:83 asarray
      
  C:\....\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:728 __array__
      &quot; array.&quot;.format(self.name))
  
  NotImplementedError: Cannot convert a symbolic Tensor (args_2:0) to a numpy array.
  </code></pre>
  <p>I do not understand where the issue is . I went to the directory where the error occurred and change the np.asarray(d) for d in data to K.asarray(d) for d in data as I saw in some solution related to incompatibility between Keras and numpy. I also tried upgrading then downgrading these 2 Libs. It did not work!
  Can some please help me in this?</p>