title: vgg16 model not converging
body: <p>I tried to train keras's VGG16 on my dataset of orca calls with 8 classes. Data contains 7 classes for orca calls and 1 class for negative. Data contains spectrogram of orca calls and their corresponding labels.</p>
  
  <p>Even after 50 epocs of training, loss and accuracy is not changing at all. I have tried feature scaling and various learning rate but model is not learning anything at all. I have checked the data manually, it is fine.</p>
  
  <p>code:</p>
  
  <pre><code>model = keras.applications.vgg16.VGG16(include_top=True, weights=None, input_tensor=None, input_shape=(320, 480, 3), pooling='max', classes=8)
  opt = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=False)
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  model.fit(x=X_train, y= Y_train, batch_size=16, epochs=2000, shuffle=True,validation_split=0.2)
  </code></pre>
  
  <p>I have read some research papers and I have done some contrasting and other type of preprocessing on images according to those papers. Upper half of images is turned to white as the frequencies I am interested in lies in second half. I chose <code>cmap ='gray'</code>, as the frequencies popped out more in it. Should I go for some another cmap? </p>
  
  <p>Some Images from my dataset:
  <a href="https://i.stack.imgur.com/zJSzJ.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/zJSzJ.jpg" alt="enter image description here"></a>
  <a href="https://i.stack.imgur.com/EfM58.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/EfM58.jpg" alt="enter image description here"></a>
  <a href="https://i.stack.imgur.com/oJue9.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/oJue9.jpg" alt="enter image description here"></a></p>
  
  <p>Kaggle kernal: <a href="https://www.kaggle.com/sainimohit23/myorcanb" rel="nofollow noreferrer">https://www.kaggle.com/sainimohit23/myorcanb</a>.</p>