我在这里问了一个关于相同代码的上一个问题where and how to put the filename in this tensorflow code?
不确定我是否应将其合并到此问题中或保留原样。
以下代码来自Sirajology的git hub。我没有找到关于如何将自己的.csv文件放入一个简单的张量流神经网络的超级直接教程,所以我希望这个帖子可以为未来的搜索者提供该指令。
代码如下
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
# Global variables.
NUM_LABELS = 2 # The number of labels.
BATCH_SIZE = 5 # The number of training examples to use per training step.
# Define the flags useable from the command line.
tf.app.flags.DEFINE_string('train', None,
'File containing the training data (labels & features).')
tf.app.flags.DEFINE_string('test', None,
'File containing the test data (labels & features).')
tf.app.flags.DEFINE_integer('num_epochs', 1,
'Number of examples to separate from the training '
'data for the validation set.')
tf.app.flags.DEFINE_boolean('verbose', False, 'Produce verbose output.')
FLAGS = tf.app.flags.FLAGS
# Extract numpy representations of the labels and features given rows consisting of:
# label, feat_0, feat_1, ..., feat_n
def extract_data(filename):
# Arrays to hold the labels and feature vectors.
labels = []
fvecs = []
# Iterate over the rows, splitting the label from the features. Convert labels
# to integers and features to floats.
for line in file(filename):
row = line.split(",")
labels.append(int(row[0]))
fvecs.append([float(x) for x in row[1:]])
# Convert the array of float arrays into a numpy float matrix.
fvecs_np = np.matrix(fvecs).astype(np.float32)
# Convert the array of int labels into a numpy array.
labels_np = np.array(labels).astype(dtype=np.uint8)
# Convert the int numpy array into a one-hot matrix.
labels_onehot = (np.arange(NUM_LABELS) == labels_np[:, None]).astype(np.float32)
# Return a pair of the feature matrix and the one-hot label matrix.
return fvecs_np,labels_onehot
def main(argv=None):
# Be verbose?
verbose = FLAGS.verbose
# Get the data.
train_data_filename = FLAGS.train
test_data_filename = FLAGS.test
# Extract it into numpy matrices.
train_data,train_labels = extract_data(train_data_filename)
test_data, test_labels = extract_data(test_data_filename)
# Get the shape of the training data.
train_size,num_features = train_data.shape
# Get the number of epochs for training.
num_epochs = FLAGS.num_epochs
# This is where training samples and labels are fed to the graph.
# These placeholder nodes will be fed a batch of training data at each
# training step using the {feed_dict} argument to the Run() call below.
x = tf.placeholder("float", shape=[None, num_features])
y_ = tf.placeholder("float", shape=[None, NUM_LABELS])
# For the test data, hold the entire dataset in one constant node.
test_data_node = tf.constant(test_data)
# Define and initialize the network.
# These are the weights that inform how much each feature contributes to
# the classification.
W = tf.Variable(tf.zeros([num_features,NUM_LABELS]))
b = tf.Variable(tf.zeros([NUM_LABELS]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
# Optimization.
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# Evaluation.
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# Create a local session to run this computation.
with tf.Session() as s:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
if verbose:
print ('Initialized!')
print
print ('Training.')
# Iterate and train.
for step in xrange(num_epochs * train_size // BATCH_SIZE):
if verbose:
print (step,)
offset = (step * BATCH_SIZE) % train_size
batch_data = train_data[offset:(offset + BATCH_SIZE), :]
batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
train_step.run(feed_dict={x: batch_data, y_: batch_labels})
if verbose and offset >= train_size-BATCH_SIZE:
print
# Give very detailed output.
if verbose:
print
print ('Weight matrix.')
print (s.run(W))
print
print ('Bias vector.')
print (s.run(b))
print
print ("Applying model to first test instance.")
first = test_data[:1]
print ("Point =", first)
print ("Wx+b = ", s.run(tf.matmul(first,W)+b))
print ("softmax(Wx+b) = ", s.run(tf.nn.softmax(tf.matmul(first,W)+b)))
print
print ("Accuracy:", accuracy.eval(feed_dict={x: test_data, y_: test_labels}))
if __name__ == '__main__':
tf.app.run()
当我使用以下命令(windows10 cmd行)python YourScript.py --train FileName.csv --test TestName.csv --num_epochs 5 --verbose True
从终端运行代码时,我收到了这些错误。非常感谢任何帮助!
错误#1 文件" softmax.py",第133行,in tf.app.run()
tf.app.run()
错误#2 文件" C:\ app.py",第43行,在运行中 sys.exit(main(sys.argv [:1] + flags_passthrough))
labels_onehot = (np.arange(NUM_LABELS) == labels_np[:, None]).astype(np.float32)
错误#3 文件" softmax.py",第57行,主要 train_data,train_labels = extract_data(train_data_filename)
train_data,train_labels = extract_data(train_data_filename)
test_data, test_labels = extract_data(test_data_filename)
错误#4 在extract_data中的文件" softmax.py",第31行 for file in file(filename): NameError:name' file'未定义
for line in file(filename):
row = line.split(",")
labels.append(int(row[7]))
fvecs.append([float(x) for x in row[1:6]])
答案 0 :(得分:1)
看起来这个问题来自于这一行,它使用了Python 3.5中没有的内置函数(file()
):
for line in file(filename):
使用以下行替换它应该可以解决错误:
for line in open(filename):