Tensorflowなぜロジスティックコストは変わらないのですか？

Question

私はテンソルフローが新しく、今は自分で定義したレイヤーで分類作業を行いたいと思っています。私は以前のいくつかのDNN層が大テンソルに出力され、次にロジスティックバイナリ分類を行うモデルを構築しました。この（しかし、RNNなし）のように： Model Structure

これは私のコードです：

import tensorflow as tf 
import numpy as np 
import pandas as pd 
from load_data import load_data_from_file 

TRAIN_FILE = 'train.csv' 
TEST_FILE = 'test.csv' 
X_train, y_train = load_data_from_file(TRAIN_FILE) 
y_train = y_train[np.newaxis] 
y_train = y_train.transpose() 
n_x_dnn = X_train.shape[1] 
n_hidden_dnn = 50 
n_merge_dnn1 = 5 * n_hidden_dnn 
n_dnn6 = 50 
hm_epochs = 10 
batch_size = 50 
X = tf.placeholder(tf.float32, [None, n_x_dnn]) 
y = tf.placeholder(tf.float32, [None, 1]) 

weights = { 
    'dnn1': tf.Variable(tf.random_normal([n_x_dnn, n_hidden_dnn])), 
    'dnn2': tf.Variable(tf.random_normal([n_hidden_dnn, n_hidden_dnn])), 
    'dnn3': tf.Variable(tf.random_normal([n_hidden_dnn, n_hidden_dnn])), 
    'dnn4': tf.Variable(tf.random_normal([n_hidden_dnn, n_hidden_dnn])), 
    'dnn5': tf.Variable(tf.random_normal([n_hidden_dnn, n_hidden_dnn])), 
    'dnn_merge': tf.Variable(tf.random_normal([n_merge_dnn1, n_dnn6])), 
    'dnn6': tf.Variable(tf.random_normal([n_dnn6, 1])) 
} 
biases = { 
    'dnn1_b': tf.Variable(tf.random_normal([n_hidden_dnn])), 
    'dnn2_b': tf.Variable(tf.random_normal([n_hidden_dnn])), 
    'dnn3_b': tf.Variable(tf.random_normal([n_hidden_dnn])), 
    'dnn4_b': tf.Variable(tf.random_normal([n_hidden_dnn])), 
    'dnn5_b': tf.Variable(tf.random_normal([n_hidden_dnn])), 
    'dnn_merge_b': tf.Variable(tf.random_normal([n_dnn6])), 
    'dnn6_b': tf.Variable(tf.random_normal([1])), 
} 


def define_layers(): 
    # dnn layer 1 
    dnn_layer1 = tf.add(tf.matmul(X, weights['dnn1']), biases['dnn1_b']) 
    dnn_layer1 = tf.nn.relu(dnn_layer1) 

    # dnn layer 2 
    dnn_layer2 = tf.add(tf.matmul(dnn_layer1, weights['dnn2']), biases['dnn2_b']) 
    dnn_layer2 = tf.nn.relu(dnn_layer2) 

    # dnn layer 3 
    dnn_layer3 = tf.add(tf.matmul(dnn_layer2, weights['dnn3']), biases['dnn3_b']) 
    dnn_layer3 = tf.nn.relu(dnn_layer3) 

    # dnn layer 4 
    dnn_layer4 = tf.add(tf.matmul(dnn_layer3, weights['dnn4']), biases['dnn4_b']) 
    dnn_layer4 = tf.nn.relu(dnn_layer4) 

    # dnn layer 5 
    dnn_layer5 = tf.add(tf.matmul(dnn_layer4, weights['dnn5']), biases['dnn5_b']) 
    dnn_layer5 = tf.nn.relu(dnn_layer5) 

    # merge layer 
    merged = tf.concat([dnn_layer1, dnn_layer2, dnn_layer3, dnn_layer4, dnn_layer5], 1) 
    dnn_merge = tf.add(tf.matmul(merged, weights['dnn_merge']), biases['dnn_merge_b']) 
    dnn_merge = tf.nn.relu(dnn_merge) 

    # dnn layer 6 
    dnn_layer6 = tf.add(tf.matmul(dnn_merge, weights['dnn6']), biases['dnn6_b']) 
    dnn_layer6 = tf.nn.sigmoid(dnn_layer6) 
    return dnn_layer6 


logits = define_layers() 
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=y)) 
optimizer = tf.train.AdamOptimizer().minimize(cost) 

saver = tf.train.Saver() 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 

    for epoch in range(hm_epochs): 
     # epoch_loss = 0 
     i = 0 
     while i < len(X_train): 
      start = i 
      end = i + batch_size 
      batch_x = np.array(X_train[start:end]) 
      batch_y = np.array(y_train[start:end]) 

      _, c, l = sess.run([optimizer, cost, logits], feed_dict={X: batch_x, 
                      y: batch_y}) 
      # epoch_loss += c 
      i += batch_size 

     # print(c, l) 
     print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', c, 'logits:', l) 

    save_path = saver.save(sess, "model/NF_dnn_and_rnn_model.ckpt") 
    print("Model saved in file: %s" % save_path) 

    X_test, y_test = load_data_from_file(TEST_FILE) 
    predict = sess.run(logits, feed_dict={X: X_test}) 
    print(predict) 

と出力から、それは私のコストは変更されませんを示しています。

Epoch 7 completed out of 10 loss: 1.27325 logits: [[ 1.] 
Epoch 8 completed out of 10 loss: 1.27325 logits: [[ 1.] 
Epoch 9 completed out of 10 loss: 1.27325 logits: [[ 1.] 

Answer 1

あなたからシグモイドの活性化を削除する必要があります最後のレイヤーdnn_layer6をsigmoid_cross_entropy_with_logitsとすると、内部的にシグモイドアクティベーションが適用されます。