python - 在这个 tensorflow 代码中如何放置文件名？

Question

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？

另外，我不明白代码是否会自动将 .csv 拆分为训练数据和测试数据，或者我是否需要在将其输入神经网络之前使用一些不同的代码来执行此操作？

我花了很多时间使用 python 和 tensorflow，并了解一些基本概念，但我仍然是一个新手。任何帮助表示赞赏!谢谢!!!

#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()

Answer 1

它期望在终端中接收它作为参数。

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.')

所以，你只需运行它:

python YourScript.py --train FileName.csv --test TestName.csv --num_epochs 5 --verbose True