Tensorflowドロップアウト実装、テスト精度=列車精度と低、なぜですか？

Question

私はTensorflowでドロップアウト実装を試みました。

トレーニング中にドロップアウトをプレースホルダとkeep_probパラメータとして宣言する必要があり、テストが異なるはずです。しかし、まだ私の脳はほとんどドロップアウトと精度を見つけるためにしようと壊した。 keep_drop = 1の場合、列車精度は99％、試験精度は85％、keep_drop = 0.5、列車と試験精度はともに16％です。ありがとうございました！

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

アルゴです。単にkeep_prob = 0.5が小さすぎます。 learning_rate = 0.00002、lambd = 0.03、train_keep_prob = 0.90、num_epochs = 1500 minibatch_size = 32、

Answer 2

最初のケースモデルで次のハイパーとテストセットに87％の精度を得ることができ

データにあまりにもフィットしていたため、列車とテスト精度の大きな違いがありました。ドロップアウトは、特定のノードの影響を減らすことによってモデルの分散を減らし、オーバーフィットを防止するための正規化手法です。しかし、keep_prob = 0.5（低すぎる）を保つとモデルが弱くなり、データに厳しいほど劣り、16％という低い精度が得られます。適切な値が見つかるまで、keep_prob値を徐々に減らして反復する必要があります。