class VGG19(tf.keras.Model):
  def __init__(self,input_shape,output_shape,**kwargs):
    super().__init__(**kwargs)
    self.conv_blocks=[
        self._conv_block(64,2),
        self._conv_block(128,2),
        self._conv_block(256,4),
        self._conv_block(512,4),
        self._conv_block(512,4),
    ]
    self.flatten = tf.keras.layers.Flatten()
    self.dense1 = tf.keras.layers.Dense(units=4096,activation="relu",kernel_regularizer=tf.keras.regularizers.L2(0.0005))
    self.dropout1 = tf.keras.layers.Dropout(0.5)
    self.dense2 = tf.keras.layers.Dense(units=4096,activation="relu",kernel_regularizer=tf.keras.regularizers.L2(0.0005))
    self.dropout2 = tf.keras.layers.Dropout(0.5)
    self.output_layer = tf.keras.layers.Dense(units=output_shape,activation="softmax")

  def _conv_block(self,filters,num_layers):
    block = tf.keras.Sequential()
    for _ in range(num_layers):
      block.add(tf.keras.layers.Conv2D(filters=filters, kernel_size=(3,3), strides=(1,1), activation="relu",padding="same"))
    block.add(tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)))
    return block

  def call(self,inputs,training=False):
    x = inputs
    for block in self.conv_blocks:
      x = block(x)
    x = self.flatten(x)
    x = self.dense1(x)
    x = self.dropout1(x,training=training)
    x = self.dense2(x)
    x= self.dropout2(x)
    x = self.output_layer(x)
    return x

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