I feel this set than before, easier to understand ~~
The issue of data downloaded in advance:
https://www.jianshu.com/p/5116046733fe
If you are using keras of cifar10.load_data () function, you will find that the code will automatically go to download cifar-10-python.tar.gz file
in fact, by looking at cifar10.py and site-packages / keras / utils / data_utils.py the get_file function, you will find that the code will be stored in the file after downloading. / under ~ keras / datasets catalog, but! ! ! ! File name was changed cifar-10-batches-py.tar.gz
No surprise surprise, meaning not an accident? So if you want to avoid downloading data has already been set up, it should:
cp CIFAR-10-python.tar.gz ~ / keras / Datasets /. CIFAR-10-Batches-py.tar.gz
Perfect to solve the problem!
Author: how children do not love to eat
link: https: //www.jianshu.com/p/5116046733fe
Source: Jane book
Jane book copyright reserved by the authors, are reproduced in any form, please contact the author to obtain authorization and indicate the source.
import timeit import tensorflow as tf import numpy as np from tensorflow.keras.datasets.cifar10 import load_data def model(): x = tf.placeholder(tf.float32, shape=[None, 32, 32, 3]) y = tf.placeholder(tf.float32, shape=[None, 10]) rate = tf.placeholder(tf.float32) # convolutional layer 1 conv_1 = tf.layers.conv2d(x, 32, [3, 3], padding='SAME', activation=tf.nn.relu) max_pool_1 = tf.layers.max_pooling2d(conv_1, [2, 2], strides=2, padding='SAME') drop_1 = tf.layers.dropout(max_pool_1, rate=rate) # convolutional layer 2 conv_2 = tf.layers.conv2d(drop_1, 64, [3, 3], padding="SAME", activation=tf.nn.relu) max_pool_2 = tf.layers.max_pooling2d(conv_2, [2, 2], strides=2, padding="SAME") drop_2 = tf.layers.dropout(max_pool_2, rate=rate) # convolutional layers 3 conv_3 = tf.layers.conv2d(drop_2, 128, [3, 3], padding="SAME", activation=tf.nn.relu) max_pool_3 = tf.layers.max_pooling2d(conv_3, [2, 2], strides=2, padding="SAME") drop_3 = tf.layers.dropout(max_pool_3, rate=rate) # fully connected layer 1 flat = tf.reshape(drop_3, shape=[-1, 4 * 4 * 128]) fc_1 = tf.layers.dense(flat, 80, activation=tf.nn.relu) drop_4 = tf.layers.dropout(fc_1 , rate=rate) # fully connected layer 2 or the output layers fc_2 = tf.layers.dense(drop_4, 10) output = tf.nn.relu(fc_2) # accuracy correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(output, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # loss loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=y)) # optimizer optimizer = tf.train.AdamOptimizer(1e-4, beta1=0.9, beta2=0.999, epsilon=1e-8).minimize(loss) return x, y, rate, accuracy, loss, optimizer def one_hot_encoder(y): ret = np.zeros(len(y) * 10) ret = ret.reshape([-1, 10]) for i in range(len(y)): ret[i][y[i]] = 1 return (ret) def train(x_train, y_train, sess, x, y, rate, optimizer, accuracy, loss): batch_size = 128 y_train_cls = one_hot_encoder(y_train) start = end = 0 for i in range(int(len(x_train) / batch_size)): if (i + 1) % 100 == 1: start = timeit.default_timer() batch_x = x_train[i * batch_size:(i + 1) * batch_size] batch_y = y_train_cls[i * batch_size:(i + 1) * batch_size] _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict={x:batch_x, y:batch_y, rate:0.4}) if (i + 1) % 100 == 0: end = timeit.default_timer() print("Time:", end-start, "s the loss is ", batch_loss, " and the accuracy is ", batch_accuracy * 100, "%") def test(x_test, y_test, sess, x, y, rate, accuracy, loss): batch_size = 64 y_test_cls = one_hot_encoder(y_test) global_loss = 0 global_accuracy = 0 for t in range(int(len(x_test) / batch_size)): batch_x = x_test[t * batch_size : (t + 1) * batch_size] batch_y = y_test_cls[t * batch_size : (t + 1) * batch_size] batch_loss, batch_accuracy = sess.run([loss, accuracy], feed_dict={x:batch_x, y:batch_y, rate:1}) global_loss += batch_loss global_accuracy += batch_accuracy global_loss = global_loss / (len(x_test) / batch_size) global_accuracy = global_accuracy / (len(x_test) / batch_size) print("In Test Time, loss is ", global_loss, ' and the accuracy is ', global_accuracy) EPOCH = 100 (x_train, y_train), (x_test, y_test) = load_data() print("There is ", len(x_train), " training images and ", len(x_test), " images") x, y, rate, accuracy, loss, optimizer = model() sess = tf.Session() sess.run(tf.global_variables_initializer()) for i in range(EPOCH): print("Train on epoch ", i ," start") train(x_train, y_train, sess, x, y, rate, optimizer, accuracy, loss) test(x_train, y_train, sess, x, y, rate, accuracy, loss)
