Tensorflow Dropout实施，测试准确性=训练准确性和低，为什么？

Question

我曾尝试在Tensorflow中实施丢包。

我的确知道，在训练和测试过程中，应该将dropout声明为占位符，keep_prob参数应该是不同的。然而，仍然几乎打破了我的大脑试图找到为什么退出的准确性是如此之低。当keep_drop = 1时，列车精度为99％，测试精度为85％，keep_drop = 0.5，列车和测试精度均为16％任何想法在哪里查看，任何人？谢谢！

def forward_propagation(X, parameters, keep_prob): 
""" 
Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> 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->RELU->LINEAR->RELU->LINEAR->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 

Answer 1

算法是正确的。这只是keep_prob = 0.5太低了。

设法在测试用下列超参数设定87％的准确度： learning_rate = 0.00002，lambd = 0.03，train_keep_prob = 0.90，num_epochs = 1500，minibatch_size = 32，

Answer 2

在第一种情况的模型过度拟合了数据，因此列车和测试精度之间的差异很大。丢失是一种正则化技术，通过减少特定节点的影响来减少模型的方差，从而防止过度拟合。但保持keep_prob = 0.5（太低）会削弱模型，因此它对数据的要求不够严格，准确度低至16％。您应该逐渐减少keep_prob值，直到找到合适的值。