Question

我在Tensorflow中尝试过dropout实现。

我知道应该将dropout声明为占位符，并且在训练和测试期间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出为什么辍学准确度如此之低。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％任何想法在哪里可以查看，有人吗？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。只是keep_prob = 0.5太低了。

使用以下超参数管理测试集的准确率达到87％： learning_rate = 0.00002，lambd = 0.03，train_keep_prob = 0.90，num_epochs = 1500，minibatch_size = 32，

Answer 2

在第一种情况下，您的模型过度拟合数据，因此列车与测试精度之间存在巨大差异。 Dropout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。但是保持keep_prob = 0.5（太低）会削弱模型，因此它会严重影响数据，从而使精度低至16％。您应该逐渐减少keep_prob值，直到找到合适的值。

Tensorflow Dropout实现，测试精度=列车精度和低，为什么？

2 个答案: