python - TensorFlow 准确度未更新
问题描述
训练准确性未更新。
可能出了什么问题?
跟着https://www.youtube.com/watch?v=yX8KuPZCAMo&t=2381s一步一步来。我什至多次验证代码匹配 100%。
尝试使用“tf.nn.softmax_cross_entropy_with_logits_v2”和“tf.nn.softmax_cross_entropy_with_logits”进行交叉熵。
我使用的数据集是 pandas Rock/Mine 数据集。我也尝试将标签列从 1/0 更改为“R”/“M”。(https://github.com/selva86/datasets/blob/master/Sonar.csv)
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
def read_dataset():
# Read CSV file.
df = pd.read_csv("c:\\users\\developer\\documents\\sonar-fixed.csv")
# print(len(df.columns))
X = df[df.columns[0:60]].values
# "R" = Rock, "M" = Mine
y = df[df.columns[60]]
# Label encoding.
encoder = LabelEncoder()
encoder.fit(y)
y = encoder.transform(y)
Y = one_hot_encode(y)
return (X, Y)
def one_hot_encode(labels):
n_labels = len(labels)
n_unique_labels = len(np.unique(labels))
one_hot_encode = np.zeros((n_labels, n_unique_labels))
one_hot_encode[np.arange(n_labels), labels] = 1
return one_hot_encode
X, Y = read_dataset()
X, Y = shuffle(X, Y, random_state=1)
train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.20, random_state=415)
print(train_x.shape)
print(train_y.shape)
print(test_x.shape)
print(test_y.shape)
learning_rate = 0.3
training_epochs = 1000
cost_history = np.empty(shape=[1], dtype=float)
n_dim = X.shape[1]
print("n_dim", n_dim)
n_class = 2
model_path = "c:\\users\\developer\\documents"
n_hidden_1 = 60
n_hidden_2 = 60
n_hidden_3 = 60
n_hidden_4 = 60
x = tf.placeholder(tf.float32, [None, n_dim])
W = tf.Variable(tf.zeros([n_dim, n_class]))
b = tf.Variable(tf.zeros([n_class]))
y_ = tf.placeholder(tf.float32, [None, n_class])
# Define the model.
def multilayer_perceptron(x, weights, biases):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.sigmoid(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.sigmoid(layer_2)
layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
layer_3 = tf.nn.sigmoid(layer_3)
layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4'])
layer_4 = tf.nn.relu(layer_4)
out_layer = tf.matmul(layer_4, weights['out'] + biases['out'])
return out_layer
# Weights and biases for each layer
weights = {
'h1': tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),
'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),
'h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),
'h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4])),
'out': tf.Variable(tf.truncated_normal([n_hidden_4, n_class])),
}
biases = {
'b1': tf.Variable(tf.truncated_normal([n_hidden_1])),
'b2': tf.Variable(tf.truncated_normal([n_hidden_2])),
'b3': tf.Variable(tf.truncated_normal([n_hidden_3])),
'b4': tf.Variable(tf.truncated_normal([n_hidden_4])),
'out': tf.Variable(tf.truncated_normal([n_class]))
}
# Initialize variables
init = tf.global_variables_initializer()
saver = tf.train.Saver()
y = multilayer_perceptron(x, weights, biases)
# Cost and optimizer
cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)
sess = tf.Session()
sess.run(init)
mse_history = []
accuracy_history = []
for epoch in range(training_epochs):
sess.run(training_step, feed_dict={x: train_x, y_: train_y})
cost = sess.run(cost_function, feed_dict={x: train_x, y_: train_y})
cost_history = np.append(cost_history, cost)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#print("accuracy", sess.run(accuracy, feed_dict={x: test_x, y_: test_y}))
pred_y = sess.run(y, feed_dict={x: test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
mse_ = sess.run(mse)
mse_history.append(mse_)
accuracy = (sess.run(accuracy, feed_dict={x: train_x, y_: train_y}))
accuracy_history.append(accuracy)
print("epoch: ", epoch, " - ", "cost: ", cost, " - MSE: ", mse_, " - accuracy: ", accuracy)
除了前两次左右的执行之外,for 循环中的准确度始终为“0.5481928”。
解决方案
推荐阅读
- mongodb - 如何强制将 mongodb 副本集实例从辅助节点变为主节点
- powerbi - 如何从 Power Query 中的列中查找最新日期 - MAX()
- android - Android 5.1 中的错误 [INSTALL_FAILED_DEXOPT]
- c++ - 原子线程围栏和消费操作
- linux - tar 另一个目录的子目录下的文件列表
- android - Android 自定义视图:是否有在 onDraw 之前调用的设置方法可以访问宽度
- python - 从 Python 核心转储获取堆栈跟踪?
- html - 服务器函数为某个电子表格返回空值到 Apps Script webapp
- c# - 表中的数据不会改变
- google-chrome - Chrome禁用一个文件的缓存