Python 在tensorflow代码中,文件名放在何处以及如何放?
我从Sirajology的github复制了一个tensorflow代码。它应该将.csv加载到单层神经网络中 我的问题是,如何以及在何处将.csv文件放入代码中 此外,我不知道代码是否会自动将.csv分割为训练和测试数据,或者在将其输入神经网络之前是否需要使用不同的代码 我花了很多时间学习python和tensorflow,了解一些基本概念,但我还是个新手。感谢您的帮助!谢谢Python 在tensorflow代码中,文件名放在何处以及如何放?,python,csv,tensorflow,Python,Csv,Tensorflow,我从Sirajology的github复制了一个tensorflow代码。它应该将.csv加载到单层神经网络中 我的问题是,如何以及在何处将.csv文件放入代码中 此外,我不知道代码是否会自动将.csv分割为训练和测试数据,或者在将其输入神经网络之前是否需要使用不同的代码 我花了很多时间学习python和tensorflow,了解一些基本概念,但我还是个新手。感谢您的帮助!谢谢 #I have eliminated all code that is obviously irrelevant to
#I have eliminated all code that is obviously irrelevant to the question
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()
它希望在终端中作为参数接收它 下面的行正在检查它:
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.')
因此,您只需将其作为:
python YourScript.py --train FileName.csv --test TestName.csv --num_epochs 5 --verbose True
谢谢,这很有道理。我正在得到一个错误“文件不违抗”,但会试图解决Sootok,如果它解决了你的问题,请接受正确的答案。我应该把你的答案标记为正确,因为它回答了这个问题,然后为新的错误信息启动一个新的线程?通过修复,我相信文件正在进入代码,但似乎还有其他问题。谢谢是的,最好把不同的问题分成不同的问题。