MNIST 与 Tensorflow，ValueError

Question

我正在尝试运行使用 Tensorflow 通过 MNIST 数据集。这是我的代码

import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

X_train = np.array(mnist.train.images, 'float')
X_test = np.array(mnist.test.images, 'float')
y_train = np.array(mnist.train.images, 'int32')
y_test = np.array(mnist.test.images, 'int32')

# Specify feature
feature_columns = [tf.contrib.layers.real_valued_column('', dimension=784)]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, 
                                            hidden_units=[200, 100, 60, 30], 
                                            n_classes=10,
                                            model_dir="./output"
                                            )

# Fit model.
classifier.fit(X_train, y_train, batch_size=100, steps=1000)

# Evaluate accuracy.
accuracy_score = classifier.evaluate(X_test, y_test)["accuracy"]

print('Accuracy: {0:f}'.format(accuracy_score))

但是，我一直收到错误消息：

ValueError: Can not squeeze dim[1], expected a dimension of 1, got 784 for 'dnn/multi_class_head/softmax_cross_entropy_loss/Squeeze' (op: 'Squeeze') with input shapes: [?,784].

回溯提醒我注意第 31 行，这是我在分类器上调用 fit() 的地方，但我不明白为什么。

Answer 1

这应该可以正常工作 tf.version = '1.1.0' 并使用 python 3.6。您的输入数据的维度可能存在一些问题，但从这里开始倒退。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

mnist_data = "save/here/mnist"

MNIST_DATASET = input_data.read_data_sets(mnist_data)

train_data = np.array(MNIST_DATASET.train.images, 'float32')
train_target = np.array(MNIST_DATASET.train.labels, 'int64')

test_data = np.array(MNIST_DATASET.test.images, 'float32')
test_target = np.array(MNIST_DATASET.test.labels, 'int64')

feature_columns = [tf.contrib.layers.real_valued_column("", dimension=784)]

classifier = tf.contrib.learn.DNNClassifier(
    feature_columns=feature_columns
    ,n_classes=10
    ,hidden_units=[128, 32]
)

classifier.fit(train_data, train_target, steps=5)
accuracy_score = classifier.evaluate(test_data, test_target, steps=5)['accuracy']
print("accuracy: ", 100*accuracy_score,"%")

输出：

WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
accuracy:  44.3899989128 %

MNIST 与 Tensorflow，ValueError

MNIST with Tensorflow, ValueError

python

mnist

tensorflow