from sklearn import preprocessing
encoder = preprocessing.LabelEncoder()
y_train = encoder.fit_transform(y_train)
如果我删除与测试文件相关的代码，它可以正常工作。但是当我添加代码来测试模型时，这就是上面代码的输出
from __future__ import print_function
import numpy as np
import tensorflow as tf
import math as math
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('dataset')
args = parser.parse_args()
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
#print(l)
#print("pass")
#print(i)
return i + 1
def read_from_csv(filename_queue):
reader = tf.TextLineReader(skip_header_lines=1)
_, csv_row = reader.read(filename_queue)
record_defaults = [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]
col0,col1, col2, col3, col4, col5,col6, col7, col8, col9, col10,col11, col12, col13, col14, col15,col16, col17, col18, col19, col20,col21, col22, col23, col24, col25,col26, col27, col28, col29, col30,col31, col32, col33, col34, col35,col36, col37, col38, col39, col40,col41, col42, col43, col44, col45,col46, col47, col48, col49, col50,col51, col52, col53, col54, col55,col56, colLabel = tf.decode_csv(csv_row, record_defaults=record_defaults)
features = tf.pack([col0,col1, col2, col3, col4, col5,col6, col7, col8, col9, col10,col11, col12, col13, col14, col15,col16, col17, col18, col19, col20,col21, col22, col23, col24, col25,col26, col27, col28, col29, col30,col31, col32, col33, col34, col35,col36, col37, col38, col39, col40,col41, col42, col43, col44, col45,col46, col47, col48, col49, col50,col51, col52, col53, col54, col55,col56])
label = tf.pack([colLabel])
return features, label
def input_pipeline(batch_size, num_epochs=1):
#filename_queue = tf.train.string_input_producer([args.dataset], num_epochs=num_epochs, shuffle=True)
[args.dataset]
filename_queue = tf.train.string_input_producer([args.dataset], num_epochs=1, shuffle=True)
example, label = read_from_csv(filename_queue)
min_after_dequeue = 4598
#capacity = min_after_dequeue + 3 * batch_size
capacity = 4599
example_batch, label_batch = tf.train.shuffle_batch(
[example, label], batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
def input_pipeline_test(batch_size, num_epochs=1):
#filename_queue = tf.train.string_input_producer([args.dataset], num_epochs=num_epochs, shuffle=True)
print("filename_queue_test")
filename_queue_test = tf.train.string_input_producer([args.dataset], num_epochs=1, shuffle=True)
example, label = read_from_csv(filename_queue_test)
print("filename_queue_test end")
min_after_dequeue = 1379
#capacity = min_after_dequeue + 3 * batch_size
capacity = 1380
example_batch, label_batch = tf.train.shuffle_batch(
[example, label], batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
print(example_batch)
return example_batch, label_batch
################################################################################
# Parameters
learning_rate = 0.6
training_epochs = 1
batch_size = 1000
display_step = 1
# Network Parameters
n_hidden_1 = 100# 1st layer number of features
n_hidden_2 = 30# 2nd layer number of features
n_input = 57 # MNIST data input (img shape: 28*28)
n_classes = 1 # MNIST total classes (0-9 digits)
# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with RELU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
################################################################################
#print("file_len(args.dataset) - 1")
#print(file_len(args.dataset) - 1)
file_length = file_len(args.dataset) - 1
#file_length = 4599
examples, labels = input_pipeline(file_length, 1)
examples_batch=tf.global_variables()
labels_batch=tf.global_variables()
examples_batch_test=tf.global_variables()
labels_batch_test=tf.global_variables()
x = tf.placeholder("float", [None, 57])
y = tf.placeholder("float", [None, 1])
pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
config=tf.ConfigProto(inter_op_parallelism_threads=2)
with tf.Session(config=config) as sess:
init_op = tf.group(tf.initialize_all_variables(),tf.initialize_local_variables())
sess.run(init_op)
#tf.initialize_all_variables().run()
# start populating filename queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop():
examples_batch, labels_batch = sess.run([examples, labels])
#x, y = sess.run([examples, labels])
except tf.errors.OutOfRangeError:
print('Done training, epoch reached')
#print(examples_batch)
#print(labels_batch)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.0
total_batch = 1
# Loop over all batches
for i in range(total_batch):
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: examples_batch,
y: labels_batch})
print("c")
print(c)
#c = {x: batch_x,y: batch_y}
# Compute average loss
#print(c / total_batch)
avg_cost = c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(avg_cost))
print("Optimization Finished!")
print("TEST BLOCK!")
try:
file_length_test=1379
examples_test, labels_test = input_pipeline_test(file_length_test, 1)
print("file print")
print(sess.run(labels_test))
print("file print end")
except:
print("file print end except")
coord.request_stop()
# with tf.Session() as sess_test:
# init_op = tf.group(tf.initialize_all_variables(),tf.initialize_local_variables())
# sess_test.run(init_op)
# #tf.initialize_all_variables().run()
# # start populating filename queue
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(coord=coord)
# print("TEST BLOCK!")
# file_length_test=1379
# examples_test, labels_test = input_pipeline_test(file_length_test, 1)
# print(examples_test)
# # start populating filename queue
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(coord=coord)
# #with tf.Session() as sess_test:
# try:
# while not coord.should_stop():
# examples_batch_test, labels_batch_test = sess_test.run([examples_test, labels_test])
# #x, y = sess_test.run([examples, labels])
# except tf.errors.OutOfRangeError:
# # Test model
# correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# # Calculate accuracy
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# print("helooOOOOO")
# print("Accuracy:", accuracy.eval({x: examples_batch_test, y: labels_test}))
# coord.request_stop()
coord.join(threads)
################################################################################
CONSOLE输出：
## CONSOLE ##
----------
The code work fine only for the train file when it tries to read test file it freeze
程序卡住了