# coding: utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import time
import create_and_read_TFRecord2 as reader2
import os
X_train, y_train = reader2.get_file("c:\\cat_and_dog_r")
image_batch, label_batch = reader2.get_batch(X_train, y_train, 227, 227, 200,
2048)
def batch_norm(inputs, is_training, is_conv_out=True, decay=0.999):
scale = tf.Variable(tf.ones([inputs.get_shape()[-1]]))
beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))
pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)
pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)
if is_training:
if is_conv_out:
batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2])
else:
batch_mean, batch_var = tf.nn.moments(inputs, [0])
train_mean = tf.assign(pop_mean,
pop_mean * decay + batch_mean * (1 - decay))
train_var = tf.assign(pop_var,
# -*- coding: utf-8 -*-
"""
Created on Sat May 5 18:09:00 2018
@author: 11420
"""
import numpy as np
import tensorflow as tf
import VGG16_model as model
import create_and_read_TFRecord2 as reader2
if __name__ == '__main__':
X_train, y_train = reader2.get_file("D:/Deep Code/defect-vs-normal/train/")
image_batch, label_batch = reader2.get_batch(X_train, y_train, 224, 224,
100, 512)
x_imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
y_imgs = tf.placeholder(tf.int32, [None, 2])
vgg = model.vgg16(x_imgs)
fc3_defect_and_normal = vgg.probs
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=fc3_defect_and_normal,
labels=y_imgs))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=1e-4).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
def onehot(labels):
n_sample = len(labels)
n_class = max(labels) + 1
onehot_labels = np.zeros((n_sample, n_class))
onehot_labels[np.arange(n_sample), labels] = 1
return onehot_labels
import VGG16_model as model
import create_and_read_TFRecord2 as reader2
if __name__ == '__main__':
print("111")
print("222")
X_train, y_train = reader2.get_file("afterClass/train") # 输入训练数据路径
image_batch, label_batch = reader2.get_batch(X_train, y_train, 224, 224,
25, 256)
x_imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
y_imgs = tf.placeholder(tf.int32, [None, 2])
vgg = model.vgg16(x_imgs)
fc3_cat_and_dog = vgg.probs
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=fc3_cat_and_dog,
labels=y_imgs))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=0.0001).minimize(loss)
pre = tf.nn.softmax(fc3_cat_and_dog)
correct_pred = tf.equal(tf.argmax(pre, 1), tf.argmax(y_imgs, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
sess = tf.Session()
import tensorflow as tf
import numpy as np
import time
import matplotlib.pyplot as plt
import create_and_read_TFRecord2 as reader2
learning_rate = 1e-4
training_iters = 2000
batch_size = 64
dispaly_step = 5
n_classes = 2
n_fc1 = 4096
n_fc2 = 2048
X_train, y_train = reader2.get_file("./data")
image_batch, label_batch = reader2.get_batch(X_train, y_train, 227, 227,
batch_size, 512)
x = tf.placeholder(tf.float32, [None, 227, 227, 3])
y = tf.placeholder(tf.int32, [None, n_classes])
W_conv = {
'conv1': tf.Variable(tf.truncated_normal([11, 11, 3, 96], stddev=0.01)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 96, 256], stddev=0.01)),
'conv3': tf.Variable(tf.truncated_normal([3, 3, 256, 384], stddev=0.01)),
'conv4': tf.Variable(tf.truncated_normal([3, 3, 384, 384], stddev=0.01)),
'conv5': tf.Variable(tf.truncated_normal([3, 3, 384, 256], stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([6 * 6 * 256, n_fc1], stddev=0.01)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.01)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.01))
}
# coding: utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import time
import create_and_read_TFRecord2 as reader2
X_train, y_train = reader2.get_file("../Pulmonary_nodules_data/train/")
image_batch, label_batch = reader2.get_batch(X_train, y_train, 227, 227, 200, 2048)
def batch_norm(inputs, is_training, is_conv_out=True, decay=0.999):
scale = tf.Variable(tf.ones([inputs.get_shape()[-1]]))
beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))
pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)
pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)
if is_training:
if is_conv_out:
batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2])
else:
batch_mean, batch_var = tf.nn.moments(inputs, [0])
train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))
train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(inputs,
batch_mean, batch_var, beta, scale, 0.001)
else: