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MorvanZhouMorvan Zhou
MorvanZhou
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Morvan Zhou
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‎tutorial-contents/401_CNN.py‎

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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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import numpy as np
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import matplotlib.pyplot as plt
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tf.set_random_seed(1)
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np.random.seed(1)
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test_x = mnist.test.images[:2000]
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test_y = mnist.test.labels[:2000]
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# plot one example
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print(mnist.train.images.shape) # (55000, 28 * 28)
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print(mnist.train.labels.shape) # (55000, 10)
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plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray')
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plt.title('%i' % np.argmax(mnist.train.labels[0]))
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plt.show()
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tf_x = tf.placeholder(tf.float32, [None, 28*28])/255. # normalize to range (0, 1)
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image = tf.reshape(tf_x, [-1, 28, 28, 1]) # (batch, height, width, channel)
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tf_y = tf.placeholder(tf.int32, [None, 10]) # input y

‎tutorial-contents/402_RNN_classification.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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numpy
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"""
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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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import numpy as np
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import matplotlib.pyplot as plt
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tf.set_random_seed(1)
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np.random.seed(1)
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# Hyper Parameters
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BATCH_SIZE = 64
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TIME_STEP = 28 # rnn time step / image height
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INPUT_SIZE = 28 # rnn input size / image width
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LR = 0.01 # learning rate
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# data
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mnist = input_data.read_data_sets('./mnist', one_hot=True)
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test_x = mnist.test.images[:2000]
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test_y = mnist.test.labels[:2000]
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# plot one example
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print(mnist.train.images.shape) # (55000, 28 * 28)
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print(mnist.train.labels.shape) # (55000, 10)
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plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray')
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plt.title('%i' % np.argmax(mnist.train.labels[0]))
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plt.show()
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# tensorflow placeholders
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tf_x = tf.placeholder(tf.float32, [None, TIME_STEP * INPUT_SIZE])/255. # shape(batch, 784), normalize to range (0, 1)
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image = tf.reshape(tf_x, [-1, TIME_STEP, INPUT_SIZE]) # (batch, height, width, channel)
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tf_y = tf.placeholder(tf.int32, [None, 10]) # input y
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# RNN
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rnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=64)
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outputs, (h_c, h_n) = tf.nn.dynamic_rnn(
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rnn_cell, # cell you have chosen
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image, # input
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initial_state=None, # the initial hidden state
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dtype=tf.float32, # must given if set initial_state = None
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time_major=False, # False: (batch, time step, input); True: (time step, batch, input)
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)
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output = tf.layers.dense(outputs[:, -1, :], 10) # output based on the last output step
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loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output) # compute cost
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train_op = tf.train.AdamOptimizer(LR).minimize(loss)
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accuracy = tf.metrics.accuracy( # return (acc, update_op), and create 2 local variables
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labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1]
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sess = tf.Session()
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init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op
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sess.run(init_op) # initialize var in graph
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for step in range(1200): # training
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b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
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_, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y})
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if step % 50 == 0: # testing
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accuracy_ = sess.run(accuracy, {tf_x: test_x, tf_y: test_y})
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print('train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_)
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# print 10 predictions from test data
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test_output = sess.run(output, {tf_x: test_x[:10]})
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pred_y = np.argmax(test_output, 1)
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print(pred_y, 'prediction number')
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print(np.argmax(test_y[:10], 1), 'real number')

‎tutorial-contents/403_RNN_regression.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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numpy
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"""
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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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# Hyper Parameters
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TIME_STEP = 10 # rnn time step
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INPUT_SIZE = 1 # rnn input size
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CELL_SIZE = 32 # rnn cell size
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LR = 0.02 # learning rate
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# show data
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steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)
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x_np = np.sin(steps) # float32 for converting torch FloatTensor
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y_np = np.cos(steps)
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plt.plot(steps, y_np, 'r-', label='target (cos)')
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plt.plot(steps, x_np, 'b-', label='input (sin)')
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plt.legend(loc='best')
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plt.show()
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# tensorflow placeholders
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tf_x = tf.placeholder(tf.float32, [None, TIME_STEP, INPUT_SIZE]) # shape(batch, 5, 1)
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tf_y = tf.placeholder(tf.float32, [None, TIME_STEP, INPUT_SIZE]) # input y
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# RNN
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rnn_cell = tf.contrib.rnn.BasicRNNCell(num_units=CELL_SIZE)
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init_s = rnn_cell.zero_state(batch_size=1, dtype=tf.float32) # very first hidden state
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outputs, final_s = tf.nn.dynamic_rnn(
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rnn_cell, # cell you have chosen
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tf_x, # input
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initial_state=init_s, # the initial hidden state
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time_major=False, # False: (batch, time step, input); True: (time step, batch, input)
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)
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outs2D = tf.reshape(outputs, [-1, CELL_SIZE]) # reshape 3D output to 2D for fully connected layer
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net_outs2D = tf.layers.dense(outs2D, INPUT_SIZE)
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outs = tf.reshape(net_outs2D, [-1, TIME_STEP, INPUT_SIZE]) # reshape back to 3D
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loss = tf.losses.mean_squared_error(labels=tf_y, predictions=outs) # compute cost
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train_op = tf.train.AdamOptimizer(LR).minimize(loss)
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sess = tf.Session()
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init_op = tf.group(tf.global_variables_initializer())
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sess.run(init_op) # initialize var in graph
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plt.figure(1, figsize=(12, 5))
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plt.ion() # continuously plot
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plt.show()
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for step in range(60):
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start, end = step * np.pi, (step+1)*np.pi # time steps
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# use sin predicts cos
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steps = np.linspace(start, end, TIME_STEP)
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x = np.sin(steps)[np.newaxis, :, np.newaxis] # shape (batch, time_step, input_size)
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y = np.cos(steps)[np.newaxis, :, np.newaxis]
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if 'final_s_' not in globals(): # first state, no any hidden state
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feed_dict = {tf_x: x, tf_y: y}
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else: # has hidden state, so pass it to rnn
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feed_dict = {tf_x: x, tf_y: y, init_s: final_s_}
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_, pred_, final_s_ = sess.run([train_op, outs, final_s], feed_dict) # train
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# plotting
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plt.plot(steps, y.flatten(), 'r-')
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plt.plot(steps, pred_.flatten(), 'b-')
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plt.ylim((-1.2, 1.2))
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plt.draw()
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plt.pause(0.05)
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plt.ioff()
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plt.show()

‎tutorial-contents/DQN.py‎

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l_target = tf.layers.dense(tf_s_, 10, tf.nn.relu)
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q_next = tf.layers.dense(l_target, N_ACTIONS)
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q_target = tf.stop_gradient(tf_r + GAMMA * tf.reduce_max(q_next, axis=1)) # shape=(None, ), not need any gradient
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q_target = tf.stop_gradient(tf_r + GAMMA * tf.reduce_max(q_next, axis=1)) # shape=(None, ), not need gradient for q_next
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a_one_hot = tf.one_hot(tf_a, depth=N_ACTIONS, dtype=tf.float32)
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q_wrt_a = tf.reduce_sum(q * a_one_hot, axis=1) # shape=(None, ), q for current state
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q_wrt_a = tf.reduce_sum(q * a_one_hot, axis=1) # shape=(None, ), q for current state
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loss = tf.reduce_mean(tf.squared_difference(q_target, q_wrt_a))
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train_op = tf.train.AdamOptimizer(LR).minimize(loss)

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