title: Expected min_ndim=4, found ndim= 2 in tensorflow
body: <p>I am developing an image classification model with images of shape (128,128,3). I have converted the image into numpy arrays but I haven't reshaped it. My model is</p>
  <pre><code>model = Sequential()
  model.add(SpatialDropout2D(0.5))
  model.add(GlobalAveragePooling2D())
  model.add(Conv2D(16, kernel_size=11, activation='relu', input_shape=(128, 128, 3)))
  model.add(Bidirectional(LSTM(16, return_sequences=True)))
  model.add(attention(return_sequences=False))
  model.add(BatchNormalization())
  model.add(Activation('tanh'))
  model.add(Dropout(0.6))
  model.add(Dense(units=2, activation='softmax'))
  model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(lr=1e-5, beta_1=0.9, eta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), metrics=['accuracy'])
  model.build(input_shape=(128,128,3))
  model.summary()
  </code></pre>
  <p>And the error I am getting is</p>
  <pre><code>    ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-64-97b1dd23f9b4&gt; in &lt;module&gt;()
       18 model.add(SpatialDropout2D(0.5,input_shape=(128, 128, 3)))
       19 model.add(GlobalAveragePooling2D())
  ---&gt; 20 model.add(Conv2D(16, kernel_size=11, activation='relu', input_shape=(128, 128, 3)))
       21 model.add(Bidirectional(LSTM(16, return_sequences=True)))
       22 model.add(attention(return_sequences=False))
  
  7 frames
  /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
      237                          ', found ndim=' + str(ndim) +
      238                          '. Full shape received: ' +
  --&gt; 239                          str(tuple(shape)))
      240     # Check dtype.
      241     if spec.dtype is not None:
  
  ValueError: Input 0 of layer conv2d_225 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 3)
  </code></pre>