initial commit

Author: xiao-data

README.md

@@ -0,0 +1,2 @@
+# lenet
+![](https://raw.githubusercontent.com/xiao-data/lenet/master/png/lenet.png)

data/t10k-images-idx3-ubyte.gz

@@ -0,0 +1,93 @@
+"""
+@author: xiao-data
+"""
+import tensorflow as tf
+import numpy as np
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("./data/", one_hot=True)
+batch_size = 128
+learning_rate = 1e-3
+display_step = 10
+test_step = 500
+num_steps = 50000
+dropout = 0.5
+l2_lambda = 1e-5
+
+X = tf.placeholder(tf.float32, [None, 28*28])
+Y = tf.placeholder(tf.float32, [None, 10])
+keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)
+
+def conv2d(x, W, b, strides=1):
+    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')
+    x = tf.nn.bias_add(x, b)
+#     return tf.nn.relu(x)
+    return tf.maximum(0.1*x,x)  #leaky relu
+
+def maxpool2d(x, k=2):
+    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')
+
+def fc(x, W, b):
+    x = tf.add(tf.matmul(x, W) , b)
+    return tf.maximum(0.1*x,x)
+#     return tf.nn.relu(x)
+#     return tf.nn.tanh(x)
+
+def lenet(X, weights, biases, dropout):
+    X = tf.reshape(X, [-1, 28, 28, 1])
+    X = tf.pad(X, [[0,0],[2,2],[2,2], [0,0]])
+    conv1 = conv2d(X, weights['conv1'], biases['conv1'])
+    pool2 = maxpool2d(conv1)
+    conv3 = conv2d(pool2, weights['conv3'], biases['conv3'])
+    pool4 = maxpool2d(conv3)
+    conv5 = conv2d(pool4, weights['conv5'], biases['conv5'])
+    conv5 = tf.contrib.layers.flatten(conv5)
+    fc6 = fc(conv5, weights['fc6'],biases['fc6'])
+    fc7 = fc(fc6, weights['fc7'],biases['fc7'])
+    fc7 = tf.nn.dropout(fc7, dropout)
+    return fc7
+
+weights = {
+    'conv1' : tf.Variable(tf.random_normal([5, 5, 1, 6])),
+    'conv3' : tf.Variable(tf.random_normal([5, 5, 6, 16])), 
+    'conv5' : tf.Variable(tf.random_normal([5, 5, 16, 120])),
+    'fc6' : tf.Variable(tf.random_normal([120, 84])),
+    'fc7' : tf.Variable(tf.random_normal([84, 10]))
+}
+biases = {
+    'conv1' : tf.Variable(tf.random_normal([6])),
+    'conv3' : tf.Variable(tf.random_normal([16])),
+    'conv5' : tf.Variable(tf.random_normal([120])),
+    'fc6' : tf.Variable(tf.random_normal([84])),
+    'fc7' : tf.Variable(tf.random_normal([10]))
+}
+
+logits = lenet(X, weights, biases, keep_prob)
+prediction = tf.nn.softmax(logits)
+
+loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
+    logits=logits, labels=Y))
+l2_loss = tf.contrib.layers.apply_regularization(regularizer=tf.contrib.layers.l2_regularizer(l2_lambda), weights_list=tf.trainable_variables())
+final_loss = loss_op + l2_loss
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+train_op = optimizer.minimize(final_loss)
+
+correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+init = tf.global_variables_initializer()
+
+with tf.Session() as sess:
+    sess.run(init)
+    X_test = mnist.test.images[:10000]
+    Y_test = mnist.test.labels[:10000]
+    for step in range(1, num_steps+1):
+        batch_x, batch_y = mnist.train.next_batch(batch_size)
+        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})
+        if step % display_step == 0 or step == 1:
+            pre,loss, acc = sess.run([prediction,loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y, keep_prob: 1.0})
+            print("Step " + str(step) + \
+                  ", Minibatch Loss= " + "{:.4f}".format(loss) + \
+                  ", Training Accuracy= " + "{:.3f}".format(acc))
+        if step % test_step == 0 and step > 10000:
+            print("Test Step "+str(step)+": Accuracy:", \
+            sess.run(accuracy, feed_dict={X: X_test, Y: Y_test,keep_prob: 1.0}))
+    print("Optimization Finished!")