Question

培训准确性没有得到更新。

出什么问题了？

逐步执行https://www.youtube.com/watch?v=yX8KuPZCAMo&t=2381s。我什至已多次验证代码匹配率100％。

尝试使用“ tf.nn.softmax_cross_entropy_with_logits_v2”和“ tf.nn.softmax_cross_entropy_with_logits”进行交叉熵计算。

我正在使用的数据集是熊猫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)

除了前两个执行之外，for循环中的精度始终为“ 0.5481928”。

TensorFlow精度未更新

0 个答案: