链接数据内容时的Tensorflow值错误-无法为Tensor'Placeholder_1:0'输入形状(1，1)的值， [英] Tensorflow value error when chaining content of data - Cannot feed value of shape (1, 1) for Tensor 'Placeholder_1:0',

问题描述

此帖子与以下问题有关.上面的代码取自可接受的答案.

This post is related to the following question. The code above is taken from the accepted answer.

程序本身可以正常工作，但是如果我仅更改

The program itself works fine as is, but if I only changed the values of the data provided from

df = pd.DataFrame({'Temperature': [183, 10.7, 24.3, 10.7],
                   'Weight': [8, 11.2, 14, 11.2],
                   'Size': [3.97, 7.88, 11, 7.88],
                   'Property': [0,1,2,0]})

到

df = pd.DataFrame({'Temperature': [0,0,0,0],
                   'Weight': [1,2,3,4],
                   'Size': [1,2,3,4],
                   'Property': [1,1,1,1]})

我在执行代码时收到以下错误

I receive the following error while executing the code

ValueError:无法输入张量的形状(1，1)的值 '占位符_1:0'，其形状为'(?，3)'

ValueError: Cannot feed value of shape (1, 1) for Tensor 'Placeholder_1:0', which has shape '(?, 3)'

在结构上什么都没有真正改变，所以这个错误让我感到非常困惑.奇怪的是，更改数据值可能会也可能不会触发此问题.我尝试了各种TF版本，包括最新版本，并且始终会出现相同的问题.

Nothing really changed structurally, so I am really confused by this error. The odd thing is that changing the values of the data may or may not trigger this issue. I've tried various TF versions including the latest and the same issue always occurs.

有人知道我在想什么吗?完整的代码示例如下.

Does anybody know what am I missing? The full code example follows.

import tensorflow as tf
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder

df = pd.DataFrame({'Temperature': [183, 10.7, 24.3, 10.7],
                   'Weight': [8, 11.2, 14, 11.2],
                   'Size': [3.97, 7.88, 11, 7.88],
                   'Property': [0,1,2,0]})


df.Property = df.Property.shift(-1)
print ( df.head() )
# parameters
time_steps = 1
inputs = 3
outputs = 3

df = df.iloc[:-1,:]

df = df.values

train_X = df[:, 1:]
train_y = df[:, 0]

scaler = MinMaxScaler(feature_range=(0, 1))    
train_X = scaler.fit_transform(train_X)

train_X = train_X[:,None,:]

onehot_encoder = OneHotEncoder()
encode_categorical = train_y.reshape(len(train_y), 1)
train_y = onehot_encoder.fit_transform(encode_categorical).toarray()

learning_rate = 0.001
epochs = 500
batch_size = int(train_X.shape[0]/2)
length = train_X.shape[0]
display = 100
neurons = 100

tf.reset_default_graph()

X = tf.placeholder(tf.float32, [None, time_steps, inputs])
y = tf.placeholder(tf.float32, [None, outputs])

cell = tf.contrib.rnn.BasicLSTMCell(num_units=neurons, activation=tf.nn.relu)
cell_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)

stacked_outputs = tf.reshape(cell_outputs, [-1, neurons])
out = tf.layers.dense(inputs=stacked_outputs, units=outputs)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
            labels=y, logits=out))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)

accuracy = tf.metrics.accuracy(labels =  tf.argmax(y, 1),
                                  predictions = tf.argmax(out, 1),
                                                            name = "accuracy")
precision = tf.metrics.precision(labels=tf.argmax(y, 1),
                                         predictions=tf.argmax(out, 1),
                                                                          name="precision")
recall = tf.metrics.recall(labels=tf.argmax(y, 1),
                                   predictions=tf.argmax(out, 1),
                                                              name="recall")
f1 = 2 * accuracy[1] * recall[1] / ( precision[1] + recall[1] )

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    tf.local_variables_initializer().run()

    for steps in range(epochs):
        mini_batch = zip(range(0, length, batch_size),
                   range(batch_size, length+1, batch_size))

        for (start, end) in mini_batch:
            sess.run(training_op, feed_dict = {X: train_X[start:end,:,:],
                                               y: train_y[start:end,:]})

        if (steps+1) % display == 0:
            loss_fn = loss.eval(feed_dict = {X: train_X, y: train_y})
            print('Step: {}  \tTraining loss: {}'.format((steps+1), loss_fn))

    acc, prec, recall, f1 = sess.run([accuracy, precision, recall, f1],
                                     feed_dict = {X: train_X, y: train_y})

    print('\nEvaluation  on training set')
    print('Accuracy:', acc[1])
    print('Precision:', prec[1])
    print('Recall:', recall[1])
    print('F1 score:', f1)

推荐答案

正如@Lescurel正确指出的那样，在分类设置中，变量output应反映目标变量中的类数.

As @Lescurel rightly pointed out, in a classification setting, the variable output should reflect the number of classes in the target variable.

而在回归设置中，它将反映目标变量的列数(假设我们预测了多个变量).

Whereas in a regression setting, it'll reflect the number of columns of the target variables (assuming we are predicting more than one variable).

给定样本输入数据:

df = pd.DataFrame({'Temperature': [1,2,3,4,5],
                   'Weight': [2,4,6,8,10],
                   'Size': [9,24,9,9,9],
                   'Property': [0,0,0,0,1]})

目标类的数量为2.因此，output = 2.

The number of target classes is 2. Hence output = 2.

注意:您在 https://paste.ubuntu中发布的代码.com/p/tmXgQfm8GB/对我来说效果很好.

Note: Your posted code in https://paste.ubuntu.com/p/tmXgQfm8GB/ works well for me.

刚发现目标变量Property是DataFrame的最后一列.

Just observed that your target variable Property is the last column of the DataFrame.

   Temperature  Weight  Size  Property
0            1       2     9       0.0
1            2       4    24       0.0
2            3       6     9       0.0
3            4       8     9       1.0
4            5      10     9       NaN

如下修改您的代码，而不是:

Modify your code as follows, instead of having:

# X_y_split
train_X = df[:, 1:]
train_y = df[:, 0]

将其更改为:

# X_y_split
train_X = df[:, :-1]
train_y = df[:, -1]

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