Add files via upload

LSTM-2ConvNet.py

@@ -0,0 +1,114 @@
+import tensorflow as tf
+import time
+
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+sess = tf.InteractiveSession()
+
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def conv2d(x, W):
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+def rnn_layer(x, timesteps, num_hidden, weights):
+    x = tf.unstack(x, timesteps, 1)
+    lstm_cell_a = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    lstm_cell_b = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(
+        lstm_cell_a, lstm_cell_b, x, dtype=tf.float32)
+    return tf.matmul(outputs[-1], weights)
+
+# Placeholders
+x = tf.placeholder(tf.float32, shape=[None, 784])
+y_ = tf.placeholder(tf.float32, shape=[None, 10])
+
+# ConvLayer 1 with max-pooling
+W_conv1 = weight_variable([5, 5, 1, 32])
+b_conv1 = bias_variable([32])
+x_image = tf.reshape(x, [-1, 28, 28, 1])
+h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+h_pool1 = max_pool_2x2(h_conv1)
+
+# ConvLayer 2 with max-pooling
+W_conv2 = weight_variable([5, 5, 32, 64])
+b_conv2 = bias_variable([64])
+h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+h_pool2 = max_pool_2x2(h_conv2)
+
+# LSTM Branch
+h_pool1_reshape = tf.transpose(h_pool1, [0, 3, 1, 2])
+h_pool1_reshape = tf.reshape(h_pool1_reshape, [-1, 32, 144])
+W_lstm = weight_variable([256, 1536])
+h_lstm = rnn_layer(h_pool1_reshape, 32, 128, W_lstm)
+
+# Dense Layer 1
+W_dense1 = weight_variable([4 * 4 * 64, 1536])
+b_dense1 = bias_variable([1536])
+h_pool2 = tf.reshape(h_pool2, [-1, 4 * 4 * 64])
+h_dense1 = tf.nn.relu(tf.matmul(h_pool2, W_dense1) + h_lstm + b_dense1)
+
+# Dense Layer 2
+W_dense2 = weight_variable([1536, 128])
+b_dense2 = bias_variable([128])
+h_dense2 = tf.nn.relu(tf.matmul(h_dense1, W_dense2) + b_dense2)
+
+# Dropout
+keep_prob = tf.placeholder(tf.float32)
+h_dense2_drop = tf.nn.dropout(h_dense2, keep_prob)
+
+# Dense Layer 3 with Softmax Output
+W_dense3 = weight_variable([128, 10])
+b_dense3 = bias_variable([10])
+y_conv = tf.matmul(h_dense2_drop, W_dense3) + b_dense3
+
+# Training Parameters
+training_rate = tf.placeholder(tf.float32)
+cross_entropy = tf.reduce_mean(
+    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
+train_step = tf.train.AdamOptimizer(training_rate).minimize(cross_entropy)
+correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+saver = tf.train.Saver(tf.global_variables())
+
+with tf.Session() as sess:
+    # Training
+    sess.run(tf.global_variables_initializer())
+    data_location = './MNIST-LSTM-2ConvNet-DATA/MNIST_LSTM_ConvNet'
+    saver.restore(sess, data_location)
+    last_time = time.time()
+    rate = 0.0001
+    for i in range(50000):
+        batch = mnist.train.next_batch(50)
+        sess.run(train_step, feed_dict={
+                 x: batch[0], y_: batch[1], keep_prob: 0.5, training_rate: rate})
+        if i % 10 == 0:
+            loss, acc = sess.run([cross_entropy, accuracy], feed_dict={
+                                 x: batch[0], y_: batch[1], keep_prob: 1.0, training_rate: rate})
+            print('Step: %d, Accuracy: %.2f, Loss: %.5f, Speed: %.1f sec/10 steps' %
+                  (i, acc, loss, time.time() - last_time))
+            last_time = time.time()                
+        if i % 250 == 0:
+            current_accuracy = accuracy.eval(
+                feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0, training_rate: rate})
+            print('- Current Test Accuracy %.4f' % current_accuracy)
+            saver.save(sess, data_location)
+            print('- Model Saved in Step %d' % i) 
+            if current_accuracy > 0.98:
+                rate = 0.00003
+            if current_accuracy > 0.99:
+                rate = 0.00001
+            if current_accuracy > 0.993:
+                rate = 0.000003
+            if current_accuracy > 0.995:
+                print('- Accuracy Reached 99.5% in Step %d' % i)
+                break 
+            last_time = time.time()

LSTM-3ConvNet.py

@@ -0,0 +1,120 @@
+import tensorflow as tf
+import time
+
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+sess = tf.InteractiveSession()
+
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def conv2d(x, W):
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+def rnn_layer(x, timesteps, num_hidden, weights):
+    x = tf.unstack(x, timesteps, 1)
+    lstm_cell_a = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    lstm_cell_b = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(
+        lstm_cell_a, lstm_cell_b, x, dtype=tf.float32)
+    return tf.matmul(outputs[-1], weights)
+
+# Placeholders
+x = tf.placeholder(tf.float32, shape=[None, 784])
+y_ = tf.placeholder(tf.float32, shape=[None, 10])
+
+# ConvLayer 1 with max-pooling
+W_conv1 = weight_variable([5, 5, 1, 48])
+b_conv1 = bias_variable([48])
+x_image = tf.reshape(x, [-1, 28, 28, 1])
+h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+h_pool1 = max_pool_2x2(h_conv1)
+
+# ConvLayer 2 with max-pooling
+W_conv2 = weight_variable([4, 4, 48, 96])
+b_conv2 = bias_variable([96])
+h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+h_pool2 = max_pool_2x2(h_conv2)
+
+# ConvLayer 3 without pooling
+W_conv3 = weight_variable([3, 3, 96, 192])
+b_conv3 = bias_variable([192])
+h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
+
+# LSTM Branch
+h_conv2_reshape = tf.transpose(h_conv2, [0, 3, 1, 2])
+h_conv2_reshape = tf.reshape(h_conv2_reshape, [-1, 96, 81])
+W_lstm = weight_variable([512, 1024])
+h_lstm = rnn_layer(h_conv2_reshape, 96, 256, W_lstm)
+
+# Dense Layer 1
+W_dense1 = weight_variable([3 * 3 * 192, 1024])
+b_dense1 = bias_variable([1024])
+h_conv3 = tf.reshape(h_conv3, [-1, 3 * 3 * 192])
+h_dense1 = tf.nn.relu(tf.matmul(h_conv3, W_dense1) + h_lstm + b_dense1)
+
+# Dense Layer 2
+W_dense2 = weight_variable([1024, 256])
+b_dense2 = bias_variable([256])
+h_dense2 = tf.nn.relu(tf.matmul(h_dense1, W_dense2) + b_dense2)
+
+# Dropout
+keep_prob = tf.placeholder(tf.float32)
+h_dense2_drop = tf.nn.dropout(h_dense2, keep_prob)
+
+# Dense Layer 3 with Softmax Output
+W_dense3 = weight_variable([256, 10])
+b_dense3 = bias_variable([10])
+y_conv = tf.matmul(h_dense2_drop, W_dense3) + b_dense3
+
+# Training Parameters
+training_rate = tf.placeholder(tf.float32)
+cross_entropy = tf.reduce_mean(
+    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
+train_step = tf.train.AdamOptimizer(training_rate).minimize(cross_entropy)
+correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+saver = tf.train.Saver(tf.global_variables())
+
+
+with tf.Session() as sess:
+    # Training
+    sess.run(tf.global_variables_initializer())
+    data_location = './MNIST-LSTM-3ConvNet-DATA/MNIST_LSTM_ConvNet'
+    saver.restore(sess, data_location)
+    last_time = time.time()
+    rate = 0.0001
+    for i in range(50000):
+        batch = mnist.train.next_batch(50)
+        sess.run(train_step, feed_dict={
+                 x: batch[0], y_: batch[1], keep_prob: 0.5, training_rate: rate})
+        if i % 10 == 0:
+            loss, acc = sess.run([cross_entropy, accuracy], feed_dict={
+                                 x: batch[0], y_: batch[1], keep_prob: 1.0, training_rate: rate})
+            print('Step: %d, Accuracy: %.2f, Loss: %.5f, Speed: %.1f sec/10 steps' %
+                  (i, acc, loss, time.time() - last_time))
+            last_time = time.time()                
+        if i % 250 == 0:
+            current_accuracy = accuracy.eval(
+                feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0, training_rate: rate})
+            print('- Current Test Accuracy %.4f' % current_accuracy)
+            saver.save(sess, data_location)
+            print('- Model Saved in Step %d' % i) 
+            if current_accuracy > 0.98:
+                rate = 0.00003
+            if current_accuracy > 0.99:
+                rate = 0.00001
+            if current_accuracy > 0.993:
+                rate = 0.000003
+            if current_accuracy > 0.995:
+                print('- Accuracy Reached 99.5% in Step %d' % i)
+                break 
+            last_time = time.time()

LSTM-ConvNet.py

@@ -0,0 +1,104 @@
+import tensorflow as tf
+import time
+
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+sess = tf.InteractiveSession()
+
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def conv2d(x, W):
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+def rnn_layer(x, timesteps, num_hidden, weights):
+    x = tf.unstack(x, timesteps, 1)
+    lstm_cell_a = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    lstm_cell_b = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
+    outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(
+        lstm_cell_a, lstm_cell_b, x, dtype=tf.float32)
+    return tf.matmul(outputs[-1], weights)
+
+# Placeholders
+x = tf.placeholder(tf.float32, shape=[None, 784])
+y_ = tf.placeholder(tf.float32, shape=[None, 10])
+
+# ConvLayer 1 with max-pooling
+W_conv1 = weight_variable([5, 5, 1, 32])
+b_conv1 = bias_variable([32])
+x_image = tf.reshape(x, [-1, 28, 28, 1])
+h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+h_pool1 = max_pool_2x2(h_conv1)
+
+# ConvLayer 2 with max-pooling
+W_conv2 = weight_variable([5, 5, 32, 64])
+b_conv2 = bias_variable([64])
+h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+
+# LSTM Layer
+h_conv2_reshape = tf.transpose(h_conv2, [0, 3, 1, 2])
+h_conv2_reshape = tf.reshape(h_conv2_reshape, [-1, 64, 64])
+W_dense1 = weight_variable([1024, 1024])
+b_dense1 = bias_variable([1024])
+h_lstm = rnn_layer(h_conv2_reshape, 64, 512, W_dense1)
+h_dense1 = tf.nn.relu(h_lstm + b_dense1)
+
+# Dropout
+keep_prob = tf.placeholder(tf.float32)
+h_dense1_drop = tf.nn.dropout(h_dense1, keep_prob)
+
+# Dense Layer 2 with Softmax Output
+W_dense2 = weight_variable([1024, 10])
+b_dense2 = bias_variable([10])
+y_conv = tf.matmul(h_dense1_drop, W_dense2) + b_dense2
+
+# Training Parameters
+training_rate = tf.placeholder(tf.float32)
+cross_entropy = tf.reduce_mean(
+    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
+train_step = tf.train.AdamOptimizer(training_rate).minimize(cross_entropy)
+correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+saver = tf.train.Saver(tf.global_variables())
+
+with tf.Session() as sess:
+    # Training
+    sess.run(tf.global_variables_initializer())
+    data_location = './MNIST-LSTM-ConvNet-DATA/MNIST_LSTM_ConvNet'
+    #saver.restore(sess, data_location)
+    last_time = time.time()
+    rate = 0.0001
+    for i in range(100000):
+        batch = mnist.train.next_batch(50)
+        sess.run(train_step, feed_dict={
+                 x: batch[0], y_: batch[1], keep_prob: 0.5, training_rate: rate})
+        if i % 10 == 0:
+            loss, acc = sess.run([cross_entropy, accuracy], feed_dict={
+                                 x: batch[0], y_: batch[1], keep_prob: 1.0, training_rate: rate})
+            print('Step: %d, Accuracy: %.2f, Loss: %.5f, Speed: %.1f sec/10 steps' %
+                  (i, acc, loss, time.time() - last_time))
+            last_time = time.time()                
+        if i % 250 == 0:
+            current_accuracy = accuracy.eval(
+                feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0, training_rate: rate})
+            print('- Current Test Accuracy %.4f' % current_accuracy)
+            saver.save(sess, data_location)
+            print('- Model Saved in Step %d' % i) 
+            if current_accuracy > 0.98:
+                rate = 0.00003
+            if current_accuracy > 0.99:
+                rate = 0.000008
+            if current_accuracy > 0.992:
+                rate = 0.000003
+            if current_accuracy > 0.995:
+                print('- Accuracy Reached 99.5% in Step %d' % i)
+                break 
+            last_time = time.time()