title: Tensorflow Dropout implementation, test accuracy = train accuracy and low, why?
body: <p>I have tried dropout implementation in Tensorflow. </p>
  
  <p>I do know that dropout should be declared as a placeholder and keep_prob parameter during training and testing should be different. However still almost broke my brain trying to find why with dropout the accuracy is so low. When keep_drop = 1, the train accuracy 99%, test accuracy 85%, with keep_drop = 0.5, both train and test accuracy is 16%  Any ideas where to look into, anyone? Thank you! </p>
  
  <pre><code>def forward_propagation(X, parameters, keep_prob):
  """
  Implements the forward propagation for the model: LINEAR -&gt; RELU -&gt; LINEAR -&gt; RELU -&gt; LINEAR -&gt; SOFTMAX
  
  Arguments:
  X -- input dataset placeholder, of shape (input size, number of examples)
  parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
                the shapes are given in initialize_parameters
  
  Returns:
  Z3 -- the output of the last LINEAR unit
  """
  # Retrieve the parameters from the dictionary "parameters" 
  W1 = parameters['W1']
  b1 = parameters['b1']
  W2 = parameters['W2']
  b2 = parameters['b2']
  W3 = parameters['W3']
  b3 = parameters['b3']
  
  
  Z1 = tf.add(tf.matmul(W1,X),b1)                     # Z1 = np.dot(W1, X) + b1
  A1 = tf.nn.relu(Z1)                                 # A1 = relu(Z1)
  A1 = tf.nn.dropout(A1,keep_prob)                    # apply dropout 
  Z2 = tf.add(tf.matmul(W2,A1),b2)                    # Z2 = np.dot(W2, a1) + b2
  A2 = tf.nn.relu(Z2)                                 # A2 = relu(Z2)
  A2 = tf.nn.dropout(A2,keep_prob)                    # apply dropout
  Z3 = tf.add(tf.matmul(W3,A2),b3)                    # Z3 = np.dot(W3,A2) + b3
  
  
  return Z3
  
  
  
  def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001, lambd = 0.03, train_keep_prob = 0.5,  
        num_epochs = 800, minibatch_size = 32, print_cost = True):
  """
  Implements a three-layer tensorflow neural network: LINEAR-&gt;RELU-&gt;LINEAR-&gt;RELU-&gt;LINEAR-&gt;SOFTMAX.
  
  Arguments:
  X_train -- training set, of shape (input size = 12288, number of training examples = 1080)
  Y_train -- test set, of shape (output size = 6, number of training examples = 1080)
  X_test -- training set, of shape (input size = 12288, number of training examples = 120)
  Y_test -- test set, of shape (output size = 6, number of test examples = 120)
  learning_rate -- learning rate of the optimization
  lambd -- L2 regularization hyperparameter
  train_keep_prob -- probability of keeping a neuron in hidden layer for dropout implementation
  num_epochs -- number of epochs of the optimization loop
  minibatch_size -- size of a minibatch
  print_cost -- True to print the cost every 100 epochs
  
  Returns:
  parameters -- parameters learnt by the model. They can then be used to predict.
  """
  
  ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
  tf.set_random_seed(1)                             # to keep consistent results
  seed = 3                                          # to keep consistent results
  (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
  n_y = Y_train.shape[0]                            # n_y : output size
  costs = []                                        # To keep track of the cost
  
  
  # Create Placeholders of shape (n_x, n_y)
  X, Y = create_placeholders(n_x, n_y)
  keep_prob = tf.placeholder(tf.float32)
  
  # Initialize parameters
  parameters = initialize_parameters()
  
  # Forward propagation: Build the forward propagation in the tensorflow graph
  Z3 = forward_propagation(X, parameters, keep_prob)
  
  # Cost function: Add cost function to tensorflow graph
  cost = compute_cost(Z3, Y, parameters, lambd)
  
  # Backpropagation. 
  optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
  
  # Initialize all the variables
  init = tf.global_variables_initializer()
  
  # Start the session to compute the tensorflow graph
  with tf.Session() as sess:
  
      # Run the initialization
      sess.run(init)
  
      # Do the training loop
      for epoch in range(num_epochs):
  
          epoch_cost = 0.                       # Defines a cost related to an epoch
          num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
          seed = seed + 1
          minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
  
          for minibatch in minibatches:
  
              # Select a minibatch
              (minibatch_X, minibatch_Y) = minibatch
  
              # IMPORTANT: The line that runs the graph on a minibatch.
              # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
              _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y, keep_prob: train_keep_prob})
  
              epoch_cost += minibatch_cost / num_minibatches
  
          # Print the cost every epoch
          if print_cost == True and epoch % 100 == 0:
              print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
          if print_cost == True and epoch % 5 == 0:
              costs.append(epoch_cost)
  
      # plot the cost
      plt.plot(np.squeeze(costs))
      plt.ylabel('cost')
      plt.xlabel('iterations (per tens)')
      plt.title("Learning rate =" + str(learning_rate))
      plt.show()
  
      # lets save the parameters in a variable
      parameters = sess.run(parameters)
      print ("Parameters have been trained!")
  
      # Calculate the correct predictions
      correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
  
      # Calculate accuracy on the test set
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
  
      print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train, keep_prob: 1.0}))
      print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test, keep_prob: 1.0}))
  
      return parameters
  </code></pre>