title: Problem in using pre-trained weights in AlexNet (Keras)
body: <p>I'm using Keras to train an AlexNet model for speech data.
  I'm going to use this model to predict a number using some wav files as input.
  I've read similar topics here like <a href="https://stackoverflow.com/questions/53897220/using-pre-trained-weights-in-alexnet-model-in-keras">this one</a> but my problem is different. My model weights have different shapes from the pre-trained weights'. 
  My pre-trained weights are from <a href="http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/" rel="nofollow noreferrer">bvlc_alexnet.npy</a>. here are my weights VS the pre-trained weights for conv layers :</p>
  
  <pre><code>conv 1 : (11, 11, 3, 96)  vs (11, 11, 3, 96)
  conv 2 : (5, 5, 96, 256)  vs (5, 5, 48, 256)
  conv 3 : (3, 3, 256, 384) vs (3, 3, 256, 384)
  conv 4 : (3, 3, 384, 384) vs (3, 3, 192, 384)
  conv 5 : (3, 3, 384, 256) vs (3, 3, 192, 256)
  </code></pre>
  
  <p>here is my model:</p>
  
  <pre><code>    model = Sequential()
  # 1st Convolutional Layer
  model.add(Conv2D(filters=96, input_shape=(277, 277, 3), kernel_size=(11, 11), strides=(4, 4),  padding="valid"))
  model.add(Activation("relu"))
  # Max Pooling
  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid"))
  
  # 2nd Convolutional Layer
  model.add(Conv2D(filters=256, kernel_size=(11, 11), strides=(1, 1), padding="valid"))
  model.add(Activation("relu"))
  # Max Pooling
  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid"))
  
  # 3rd Convolutional Layer
  model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding="valid"))
  model.add(Activation("relu"))
  
  # 4th Convolutional Layer
  model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding="valid"))
  model.add(Activation("relu"))
  
  # 5th Convolutional Layer
  model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding="valid"))
  model.add(Activation("relu"))
  # Max Pooling
  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid"))
  
  # Fully Connected layers
  model.add(Flatten())
  # 1st Fully Connected Layer
  model.add(Dense(4096, input_shape=(277 * 277 * 3,)))
  model.add(Activation("relu"))
  model.add(Dropout(0.4))
  
  # 2nd Fully Connected Layer
  model.add(Dense(4096))
  model.add(Activation("relu"))
  model.add(Dropout(0.4))
  
  # 3rd Fully Connected Layer
  model.add(Dense(1000))
  model.add(Activation("relu"))
  model.add(Dropout(0.4))
  
  # Output Layer
  model.add(Dense(10))
  model.add(Activation("softmax"))
  </code></pre>