我在数据集上运行神经网络算法但遇到错误。我不确定这个错误是什么意思或如何解决它。数据集很简单 1 0 1 0 1 0等等，第三列将是我正在分类的类别。我的代码如下：
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< [28] .shstrtab STRTAB 0000000000000000 0000189f
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> [28] .shstrtab STRTAB 0000000000000000 000018a0
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< 0000000000000207 0000000000000000 0 0 1
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> 0000000000000208 0000000000000000 0 0 1
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< 37: 0000000000000000 0 FILE LOCAL DEFAULT ABS FIRST_PROG.c
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> 37: 0000000000000000 0 FILE LOCAL DEFAULT ABS SECOND_PROG.c
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< Build ID: 2c64961288049002e34a1f14e55d6c80dd96816c
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> Build ID: 5425dec81aae53bd30e85fe94659d320bb774dcc
当我运行此代码时，我收到from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import urllib
import tensorflow as tf
import numpy as np
Stock_TRAINING = "Stocks.csv"
Stock_TEST = "Workbook2.csv"
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=Stock_TRAINING,
target_dtype=np.int,
features_dtype=np.int)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=Stock_TEST,
target_dtype=np.int,
features_dtype=np.int)
# Specify that all features have real-value data
feature_columns = [tf.feature_column.numeric_column("x", shape=[2])]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=2,
model_dir="/tmp/stocks")
# Define the training inputs
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(training_set.data)},
y=np.array(training_set.target),
num_epochs=None,
shuffle=True)
# Train model.
classifier.train(input_fn=train_input_fn, steps=2000)
# Define the test inputs
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(test_set.data)},
y=np.array(test_set.target),
num_epochs=1,
shuffle=False)
# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
# Classify two new stock samples.
new_samples = np.array(
[[1, 0],
[1,1]
[0,0],
[0,1],
[1, 1]], dtype=np.float32)
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": new_samples},
num_epochs=1,
shuffle=False)
predictions = list(classifier.predict(input_fn=predict_input_fn))
predicted_classes = [p["UpDown"] for p in predictions]
print("Stock is Up or Down Next Day: {}\n"
.format(predicted_classes))
classifier.train(input_fn=train_input_fn, steps=2000)如果您需要查看完整的错误消息或需要其他任何内容，请与我们联系。