ValueError：无法为具有形状“（？，2）”的张量“Placeholder_11：0”提供形状（165，）的值

Question

我只是在学习 TensorFlow。该程序的目标是检测地雷和岩石。但是当我喂占位符时我遇到了问题。我在这个网站上阅读了很多关于这个问题的问题，但无法找到解决方案。

在feed_dict 中train_y 的形状是(165,) 和y（占位符）是(?, 2)...这是我认为的问题。但我不知道如何解决它。我试图重塑train_y，但它不起作用。

我有这个错误：

ValueError: 无法为张量提供形状 (165,) 的值 'Placeholder_11:0'，形状为 '(?, 2)'

这是data 和我的程序：

import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split


#traitement des données
df = pd.read_csv('sonar.all-data.csv')
X = df[df.columns[:60]].values
y = df[df.columns[60]]
encoder = LabelEncoder()
encoder.fit(y)
y = encoder.transform(y)


#mix data
X,y = shuffle(X, y, random_state = 1)

#separate date for training
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 415)




# Parameters
learning_rate = 0.1
num_steps = 500
display_step = 100


# Network Parameters
n_hidden_1 = 60 # 1st layer number of neurons 60
n_hidden_2 = 60 # 2nd layer number of neurons 60
num_input = X.shape[1]
num_classes = 2



# tf Graph input
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])



# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([num_classes]))
}



# Create model
def neural_net(x):
    # Hidden fully connected layer with 50 neurons
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Hidden fully connected layer with 50 neurons
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    # Output fully connected layer with a neuron for each class
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    out_layer = tf.nn.relu(out_layer)
    
    return out_layer


# Construct model
logits = neural_net(X)
prediction = tf.nn.softmax(logits)



# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)


# Evaluate model
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()




# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    for step in range(1, num_steps+1):
        sess.run(train_op, feed_dict={X: train_x, Y: train_y})
        if step % display_step == 0 or step == 1:
            # Calculate loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: train_x,
                                                                 Y: train_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

感谢您的帮助！

Answer 1

我认为您应该对标签应用一次性编码 y：将您的 LabelEncoder 替换为 sklearn.preprocessing.OneHotEncoder。

此代码应该适用于您的数据：

y = df[df.columns[60]].apply(lambda x: 0 if x == 'R' else 1).values.reshape(-1, 1)
encoder = OneHotEncoder()
encoder.fit(y)
y = encoder.transform(y)