title: Keras Unet + VGG16 predictions are all the same
body: <p>I am training U-Net with VGG16 (decoder part) in Keras. The model trains well and is learning - I see gradua tol improvement on validation set.</p>
  
  <p>However, when I try to call <code>predict</code> on images, I receive matrix which has all values the same.</p>
  
  <p>Below is the model:</p>
  
  <pre><code>class Gray2VGGInput(Layer):
      """Custom conversion layer"""
      def build(self, x):
          self.image_mean = K.variable(value=np.array([103.939, 116.779, 123.68]).reshape([1,1,1,3]).astype('float32'), 
                                       dtype='float32', 
                                       name='imageNet_mean' )
          self.built = True
          return
      def call(self, x):
          rgb_x = K.concatenate([x,x,x], axis=-1 )
          norm_x = rgb_x - self.image_mean
          return norm_x
  
      def compute_output_shape(self, input_shape):
          return input_shape[:3] + (3,)
  
  
  def UNET1_VGG16(img_rows=864, img_cols=1232):
      ''' 
      UNET with pretrained layers from VGG16
      '''
      def upsampleLayer(in_layer, concat_layer, input_size):
          '''
          Upsampling (=Decoder) layer building block
          Parameters
          ----------
          in_layer: input layer
          concat_layer: layer with which to concatenate
          input_size: input size fot convolution
          '''
          upsample = Conv2DTranspose(input_size, (2, 2), strides=(2, 2), padding='same')(in_layer)    
          upsample = concatenate([upsample, concat_layer])
          conv = Conv2D(input_size, (1, 1), activation='relu', kernel_initializer='he_normal', padding='same')(upsample)
          conv = BatchNormalization()(conv)
          conv = Dropout(0.2)(conv)
          conv = Conv2D(input_size, (1, 1), activation='relu', kernel_initializer='he_normal', padding='same')(conv)
          conv = BatchNormalization()(conv)
          return conv
  
      #--------
      #INPUT
      #--------
      #batch, height, width, channels
      inputs_1 = Input((img_rows, img_cols, 1))
  
      #-----------------------
      #INPUT CONVERTER &amp; VGG16
      #-----------------------
      inputs_3 = Gray2VGGInput(name='gray_to_rgb')(inputs_1)  #shape=(img_rows, img_cols, 3)
      base_VGG16 = VGG16(include_top=False, weights='imagenet', input_tensor=inputs_3)
  
      #--------
      #DECODER
      #--------
      c1 = base_VGG16.get_layer("block1_conv2").output #(None, 864, 1232, 64)
      c2 = base_VGG16.get_layer("block2_conv2").output #(None, 432, 616, 128) 
      c3 = base_VGG16.get_layer("block3_conv2").output #(None, 216, 308, 256) 
      c4 = base_VGG16.get_layer("block4_conv2").output #(None, 108, 154, 512) 
  
      #--------
      #BOTTLENECK
      #--------
      c5 = base_VGG16.get_layer("block5_conv2").output #(None, 54, 77, 512)
  
      #--------
      #ENCODER
      #--------    
      c6 = upsampleLayer(in_layer=c5, concat_layer=c4, input_size=512)
      c7 = upsampleLayer(in_layer=c6, concat_layer=c3, input_size=256)
      c8 = upsampleLayer(in_layer=c7, concat_layer=c2, input_size=128)
      c9 = upsampleLayer(in_layer=c8, concat_layer=c1, input_size=64)
  
      #--------
      #DENSE OUTPUT
      #--------
      outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
  
      model = Model(inputs=inputs_1, outputs=outputs)
  
      #Freeze layers
      for layer in model.layers[:16]:
          layer.trainable = False
  
      print(model.summary())
  
      model.compile(optimizer='adam', 
                    loss=fr.diceCoefLoss, 
                    metrics=[fr.diceCoef])
  
      return model
  </code></pre>
  
  <p>Then, I load the model and call <code>predict</code>:</p>
  
  <pre><code>model = un.UNET1_VGG16()
  
  pth_to_model = PTH_OUTPUT + 'weights__L_01.h5'
  model.load_weights(pth_to_model) 
  
  preds = model.predict(X_image_test, verbose=1)
  </code></pre>
  
  <p>However, the result looks as follows:</p>
  
  <pre><code>[[0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]
   [0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]
   [0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]
   ...
   [0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]
   [0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]
   [0.4567569 0.4567569 0.4567569 ... 0.4567569 0.4567569 0.4567569]]
  </code></pre>
  
  <p>I am using the same procedure with other models without VGG16 and everything works well. Thus, I assume something related to the VGG16 is wrong. Perhaps the input layer, which I am converting to "fake" RGB image?</p>