Question

您好Tensorflow的专家！

我主要从示例中复制以下代码。我的NN中有38个输入和4个输出。我想教NN并做出预测。但如果我运行该程序，我会收到一个错误说：

b = tf.Variable（tf.float32，[None，n_class]） TypeError：预期的二进制或unicode字符串，得到tf.float32

与：

TypeError：无法将类型对象转换为Tensor。内容：。考虑将元素转换为支持的类型。

import csv
import matplotlib as plt
import tensorflow as tf
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

with open('input.csv', "r") as myfile:
    reader = csv.reader(myfile, delimiter=",")
    data = list(reader)
    data = np.asarray(data)

x = data[:,0:39]
y = data[:,39:43]

train_x, test_x, train_y, test_y = train_test_split(x,y, test_size=0.05)

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 =

n_hidden_1 = 60
n_hidden_2 = 60
n_hidden_3 = 60

X = tf.placeholder(tf.float32, [None, n_dim])
W = tf.Variable(tf.zeros([n_dim, n_class]))
b = tf.Variable(tf.float32, [None, n_class])
y_ = tf.placeholder(tf.float32, [None, n_class])

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.relu(layer_3)

    out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
    return out_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])),
    'out': tf.Variable(tf.truncated_normal([n_hidden_3, 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])),
    'out' : tf.Variable(tf.truncated_normal([n_class]))
}

init = tf.global_variables_initializer()

saver = tf.train.Saver()

cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_func)

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_func, 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))

    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_, " - Train Accuracy: ", accuracy)

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test 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)))
print ("MSE %.4f" % sess.run(mse))

您有什么建议吗？

Tensorflow Python Type错误让我明白什么是错的

0 个答案: