Question

import tensorflow as tf
import os
import sklearn.preprocessing
import pandas as pd
import numpy as np

print(os.getcwd())
os.chdir("C:/Users/jbnu/Documents/양지성/Scholar/정규학기/3-2/데이터마이닝실습/프로젝트/현행/bank-additional/bank-additional")

导入和管理数据集

bank = pd.read_csv("bank4.csv", index_col=False)

tf.reset_default_graph()
keep_prob = tf.placeholder(tf.float32)
learning_rate = 0.003

x_data = bank.ix[:,0:9]; print(x_data)
y_data = bank.ix[:, [-1]]; print(y_data)
x_data = sklearn.preprocessing.scale(x_data).astype(np.float32); print(x_data)
y_data = y_data.astype(np.float32)

使用3层设置占位符和权重。

X = tf.placeholder(tf.float32, [None, 9]); print(X)
Y = tf.placeholder(tf.float32, [None, 1])

# Layer 1
W1 = tf.get_variable("weight1", shape=[9,15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.get_variable("bias1", shape=[15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
layer1 = tf.nn.relu(tf.matmul(X, W1) + b1)
layer1 = tf.nn.dropout(layer1, keep_prob=keep_prob)

# Layer 2
W2 = tf.get_variable("weight2", shape=[15,15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.get_variable("bias2", shape=[15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
layer2 = tf.nn.relu(tf.matmul(layer1, W2) + b2)
layer2 = tf.nn.dropout(layer2, keep_prob=keep_prob)

# Layer 3
W3 = tf.get_variable("weight3", shape=[15,15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.get_variable("bias3", shape=[15], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
layer3 = tf.nn.relu(tf.matmul(layer2, W3) + b3)
layer3 = tf.nn.dropout(layer3, keep_prob=keep_prob)

# Output Layer
W4 = tf.get_variable("weight4", shape=[15,1], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
b4 = tf.get_variable("bias4", shape=[1], dtype = tf.float32,
                     initializer=tf.contrib.layers.xavier_initializer())
hypothesis = tf.sigmoid(tf.matmul(layer3, W4) + b4)
hypothesis = tf.nn.dropout(hypothesis, keep_prob=keep_prob)

定义成本函数和优化器。

cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))

train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

培训和准确性测试

# Launch graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for step in range(10001):
        sess.run(train, feed_dict={X: x_data, Y: y_data})
        if step % 1000 == 0:
            print("step: ", step, sess.run(cost, feed_dict={X: x_data, Y: y_data}), sep="\n")

    # Accuracy report
    h, c, a = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)

我不知道为什么我的NN不起作用。

我不断收到消息＆＃34;您必须为占位符张量和占位符＆＃39;提供值。与dtype float＆＃34;虽然它们都是float32。

另外，我的辍学率遇到feed_dict错误。请运行代码并告诉我错误。

Answer 1

它抱怨辍学keep_prob占位符：

keep_prob = tf.placeholder(tf.float32)

您应该在feed_dict以及X和Y中提供，或者将其设为tf.placeholder_with_default，如果您不想将其全部通过时间。

您必须为占位符张量值占位符＆＃39; Placeholder＆＃39;与dtype float（Tensorflow）

1 个答案: