Tensorflow catdog-checkpoint-白红宇

import tensorflow as tfimport globimport numpy as np复制代码

/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5  return f(*args, **kwds)/anaconda3/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.  from ._conv import register_converters as _register_converters复制代码

image_filenames = glob.glob('./dc/train/*.jpg')复制代码

image_filenames[0]复制代码

---------------------------------------------------------------------------IndexError                                Traceback (most recent call last)
    
      in 
     
      ()----> 1 image_filenames[0]IndexError: list index out of range复制代码

lables = list(map(lambda x : x.split('\\')[1].split('.')[0], image_filenames))复制代码

train_lable = [[1, 0] if x == 'cat' else [0, 1] for x in lables]复制代码

image_que = tf.train.slice_input_producer([image_filenames, train_lable])复制代码

image_ = tf.read_file(image_que[0])image = tf.image.decode_jpeg(image_, channels = 3)复制代码

grey_image = tf.image.rgb_to_grayscale(image)resize_image = tf.image_resize_images(grey_image, (200, 200))resize_image = tf.reshape(resize_image, [200, 200, 1])复制代码

new_img = tf.image.per_image_standardization(resize_image)复制代码

batch_size = 60capacity = 10 + 2 * batch_size复制代码

image_batch, lable_batch = tf.train.batch([new_img, image_que[1]], batch_size=batch_size, capacity=capacity)复制代码

lable_batch.get_shape()复制代码

conv2d_1 = tf.contrib.layers.convolution2d(    image_batch,    num_outputs=32,    weights_initializer = tf.truncated_normal_initializer(stddev=0.001),    kernel_size = (5,5),    activation_fn = tf.nn.relu,    stride = (1,1),    padding = 'SAME',    trainable = True)pool_1 = tf.nn.max_pool(conv2d_1,                       ksize = [1,3,3,1],                       strides = [1,2,2,1],                       padding='SAME')复制代码

conv2d_2 = tf.contrib.layers.convolution2d(    pool_1,    num_outputs=32,    weights_initializer = tf.truncated_normal_initializer(stddev=0.01),    kernel_size = (5,5),    activation_fn = tf.nn.relu,    stride = (1,1),    padding = 'SAME',    trainable = True)pool_2 = tf.nn.max_pool(conv2d_2,                       ksize = [1,3,3,1],                       strides = [1,2,2,1],                       padding='SAME')复制代码

conv2d_3 = tf.contrib.layers.convolution2d(    pool_2,    num_outputs=64,    weights_initializer = tf.truncated_normal_initializer(stddev=0.01),    kernel_size = (5,5),    activation_fn = tf.nn.relu,    stride = (1,1),    padding = 'SAME',    trainable = True)pool_3 = tf.nn.max_pool(conv2d_3,                       ksize = [1,3,3,1],                       strides = [1,2,2,1],                       padding='SAME')复制代码

pool_3.get_shape()复制代码

pool3_flat = tf.reshape(pool_3, [-1, 25*25*64])fc_1 = tf.contrib.layers.fully_connected(                            pool3_flat,                             1024,                             weights_initializer = tf.truncated_normal_initializer(stddev=0.1),                            activation_fn = tf.nn.relu)复制代码

fc_2 = tf.contrib.layers.fully_connected(                            fc_1,                             192,                             weights_initializer = tf.truncated_normal_initializer(stddev=0.1),                            activation_fn = tf.nn.relu)复制代码

out_wl = tf.Variable(tf.truncated_normal([192, 2]))out_bl = tf.Variable(tf.truncated_normal([2]))comb_out = tf.matmul(fc_2, out_wl) + out_blpred = tf.sigmoid(comb_out)复制代码

pred.get_shape()复制代码

lable_batch.get_shape()复制代码

lable_batch = tf.cast(lable_batch, tf.float32)复制代码

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = lable_batch, logits = comb_out))复制代码

train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)复制代码

predicted = tf.cast(pred >0.5, tf.float32)accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, lable_batch), tf.float32))复制代码

saver = tf.train.Saver()with tf.Session() as sess:    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(coord = coord)    sess.run(tf.global_variables_initializer())    for step in range(0, 3000):        sess.run(train_step)        if(step %100 == 0):            res = sess.run([loss, accuracy])            print(step, res)            saver.save(sess, './lesson30', global_step = step)    coord.request_stop()    coord.join(threads)复制代码

恢复检查点

#import os#ckpt = tf.train_get_checkpoint_state(os.path.dirname('__file__'))#saver = tf.train.Saver()#sess = tf.Session()#sess.run(tf.global_variables_initializer())#saver.restore(sess, ckpt.model_checkpoint_path)复制代码

#coord = tf.train.Coordinator()#threads = tf.train.start_queue_runners(coord = coord)#for step in range(6000, 7000):#        sess.run(train_step, feed_dict={keep_prob:0.5})#        if(step %100 == 0):#            res = sess.run([loss, accuracy], feed_dict={keep_prob:1})#            print(step, res)#            saver.save(sess, './lesson30', global_step = step)#coord.request_stop()#coord.join(threads)复制代码