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| Dependencies: | ||
| tensorflow: 1.1.0 | ||
| matplotlib | ||
| numpy | ||
| """ | ||
| import tensorflow as tf | ||
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| """ | ||
| Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/ | ||
| My Youtube Channel: https://www.youtube.com/user/MorvanZhou | ||
| Dependencies: | ||
| tensorflow: 1.1.0 | ||
| """ | ||
| import tensorflow as tf | ||
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| var = tf.Variable(0) # our first variable in the "global_variable" set | ||
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| add_operation = tf.add(var, 1) | ||
| update_operation = tf.assign(var, add_operation) | ||
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| with tf.Session() as sess: | ||
| # once define variables, you have to initialize them by doing this | ||
| sess.run(tf.global_variables_initializer()) | ||
| for _ in range(3): | ||
| sess.run(update_operation) | ||
| print(sess.run(var)) |
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| """ | ||
| Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/ | ||
| My Youtube Channel: https://www.youtube.com/user/MorvanZhou | ||
| Dependencies: | ||
| tensorflow: 1.1.0 | ||
| matplotlib | ||
| """ | ||
| import tensorflow as tf | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
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| # fake data | ||
| x = np.linspace(-5, 5, 200) # x data, shape=(100, 1) | ||
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| # following are popular activation functions | ||
| y_relu = tf.nn.relu(x) | ||
| y_sigmoid = tf.nn.sigmoid(x) | ||
| y_tanh = tf.nn.tanh(x) | ||
| y_softplus = tf.nn.softplus(x) | ||
| # y_softmax = tf.nn.softmax(x) softmax is a special kind of activation function, it is about probability | ||
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| sess = tf.Session() | ||
| y_relu, y_sigmoid, y_tanh, y_softplus = sess.run([y_relu, y_sigmoid, y_tanh, y_softplus]) | ||
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| # plt to visualize these activation function | ||
| plt.figure(1, figsize=(8, 6)) | ||
| plt.subplot(221) | ||
| plt.plot(x, y_relu, c='red', label='relu') | ||
| plt.ylim((-1, 5)) | ||
| plt.legend(loc='best') | ||
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| plt.subplot(222) | ||
| plt.plot(x, y_sigmoid, c='red', label='sigmoid') | ||
| plt.ylim((-0.2, 1.2)) | ||
| plt.legend(loc='best') | ||
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| plt.subplot(223) | ||
| plt.plot(x, y_tanh, c='red', label='tanh') | ||
| plt.ylim((-1.2, 1.2)) | ||
| plt.legend(loc='best') | ||
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| plt.subplot(224) | ||
| plt.plot(x, y_softplus, c='red', label='softplus') | ||
| plt.ylim((-0.2, 6)) | ||
| plt.legend(loc='best') | ||
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| plt.show() |
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| """ | ||
| Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/ | ||
| My Youtube Channel: https://www.youtube.com/user/MorvanZhou | ||
| Dependencies: | ||
| tensorflow: 1.1.0 | ||
| matplotlib | ||
| numpy | ||
| """ | ||
| import tensorflow as tf | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
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| tf.set_random_seed(1) | ||
| np.random.seed(1) | ||
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| # fake data | ||
| x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1) | ||
| noise = np.random.normal(0, 0.1, size=x.shape) | ||
| y = np.power(x, 2) + noise # shape (100, 1) + some noise | ||
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| def save(): | ||
| print('This is save') | ||
| # build neural network | ||
| tf_x = tf.placeholder(tf.float32, x.shape) # input x | ||
| tf_y = tf.placeholder(tf.float32, y.shape) # input y | ||
| l = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer | ||
| o = tf.layers.dense(l, 1) # output layer | ||
| loss = tf.losses.mean_squared_error(tf_y, o) # compute cost | ||
| train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss) | ||
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| sess = tf.Session() | ||
| sess.run(tf.global_variables_initializer()) # initialize var in graph | ||
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| saver = tf.train.Saver() # define a saver for saving and restoring | ||
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| for step in range(100): # train | ||
| sess.run(train_op, {tf_x: x, tf_y: y}) | ||
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| saver.save(sess, 'params', write_meta_graph=False) # meta_graph is not recommended | ||
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| # plotting | ||
| pred, l = sess.run([o, loss], {tf_x: x, tf_y: y}) | ||
| plt.figure(1, figsize=(10, 5)) | ||
| plt.subplot(121) | ||
| plt.scatter(x, y) | ||
| plt.plot(x, pred, 'r-', lw=5) | ||
| plt.text(-1, 1.2, 'Save Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'}) | ||
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| def reload(): | ||
| print('This is reload') | ||
| # build entire net again and restore | ||
| tf_x = tf.placeholder(tf.float32, x.shape) # input x | ||
| tf_y = tf.placeholder(tf.float32, y.shape) # input y | ||
| l_ = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer | ||
| o_ = tf.layers.dense(l_, 1) # output layer | ||
| loss_ = tf.losses.mean_squared_error(tf_y, o_) # compute cost | ||
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| sess = tf.Session() | ||
| # don't need to initialize variables, just restoring trained variables | ||
| saver = tf.train.Saver() # define a saver for saving and restoring | ||
| saver.restore(sess, 'params') | ||
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| # plotting | ||
| pred, l = sess.run([o_, loss_], {tf_x: x, tf_y: y}) | ||
| plt.subplot(122) | ||
| plt.scatter(x, y) | ||
| plt.plot(x, pred, 'r-', lw=5) | ||
| plt.text(-1, 1.2, 'Reload Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'}) | ||
| plt.show() | ||
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| save() | ||
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| # destroy previous net | ||
| tf.reset_default_graph() | ||
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| reload() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| """ | ||
| Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/ | ||
| My Youtube Channel: https://www.youtube.com/user/MorvanZhou | ||
| Dependencies: | ||
| tensorflow: 1.1.0 | ||
| matplotlib | ||
| numpy | ||
| """ | ||
| import tensorflow as tf | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
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| tf.set_random_seed(1) | ||
| np.random.seed(1) | ||
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| LR = 0.01 | ||
| BATCH_SIZE = 32 | ||
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| # fake data | ||
| x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1) | ||
| noise = np.random.normal(0, 0.1, size=x.shape) | ||
| y = np.power(x, 2) + noise # shape (100, 1) + some noise | ||
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| # plot dataset | ||
| plt.scatter(x, y) | ||
| plt.show() | ||
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| # default network | ||
| class Net: | ||
| def __init__(self, opt, **kwargs): | ||
| self.x = tf.placeholder(tf.float32, [None, 1]) | ||
| self.y = tf.placeholder(tf.float32, [None, 1]) | ||
| l = tf.layers.dense(self.x, 20, tf.nn.relu) | ||
| out = tf.layers.dense(l, 1) | ||
| self.loss = tf.losses.mean_squared_error(self.y, out) | ||
| self.train = opt(LR, **kwargs).minimize(self.loss) | ||
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| # different nets | ||
| net_SGD = Net(tf.train.GradientDescentOptimizer) | ||
| net_Momentum = Net(tf.train.MomentumOptimizer, momentum=0.9) | ||
| net_RMSprop = Net(tf.train.RMSPropOptimizer) | ||
| net_Adam = Net(tf.train.AdamOptimizer) | ||
| nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam] | ||
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| sess = tf.Session() | ||
| sess.run(tf.global_variables_initializer()) | ||
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| losses_his = [[], [], [], []] # record loss | ||
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| # training | ||
| for step in range(300): # for each training step | ||
| index = np.random.randint(0, x.shape[0], BATCH_SIZE) | ||
| b_x = x[index] | ||
| b_y = y[index] | ||
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| for net, l_his in zip(nets, losses_his): | ||
| _, l = sess.run([net.train, net.loss], {net.x: b_x, net.y: b_y}) | ||
| l_his.append(l) # loss recoder | ||
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| # plot loss history | ||
| labels = ['SGD', 'Momentum', 'RMSprop', 'Adam'] | ||
| for i, l_his in enumerate(losses_his): | ||
| plt.plot(l_his, label=labels[i]) | ||
| plt.legend(loc='best') | ||
| plt.xlabel('Steps') | ||
| plt.ylabel('Loss') | ||
| plt.ylim((0, 0.2)) | ||
| plt.show() |
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