title: Trying to understand how to add more hidden layers into a neural network using a for loop
body: <p>I'm trying to figure out how would I make a simple for loop to add more hidden layers to this neural network for the basic tensorflow neural network from the code below:</p>
  
  <pre><code>import tensorflow as tf
  mnist = tf.keras.datasets.mnist
  
  (x_train, y_train),(x_test, y_test) = mnist.load_data()
  x_train, x_test = x_train / 255.0, x_test / 255.0
  
  model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])
  
  model.compile(optimizer='adam',
            loss='sparse_categorical_crossentropy',
            metrics=['accuracy'])
  
  model.fit(x_train, y_train, epochs=5)
  model.evaluate(x_test, y_test)
  </code></pre>
  
  <p>normally I would go ahead and change the following code:</p>
  
  <pre><code>model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])
  </code></pre>
  
  <p>and add more layers.Dense as follow:</p>
  
  <pre><code>model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])
  </code></pre>
  
  <p>Is it possible to create a simple for loop where I can input the number of hidden layers I want?</p>