upload keras tutorial codes

Author: MorvanZhou

kerasTUT/2-installation.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 2 - Installation
+
+"""
+---------------------------
+1. Make sure you have installed the following dependencies for Keras:
+- Numpy
+- Scipy
+
+for install numpy and scipy, please refer to my video tutorial:
+https://www.youtube.com/watch?v=JauGYB-Bzuw&list=PLXO45tsB95cKKyC45gatc8wEc3Ue7BlI4&index=2
+---------------------------
+2. run 'pip install keras' in command line for python 2+
+Or 'pip3 install keras' for python 3+
+
+If encounter the error related to permission, then use 'sudo pip install ***'
+---------------------------
+
+"""

kerasTUT/3-backend.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 3 - backend
+
+
+"""
+Details are showing in the video.
+
+----------------------
+Method 1:
+If you have run Keras at least once, you will find the Keras configuration file at:
+
+~/.keras/keras.json
+
+If it isn't there, you can create it.
+
+The default configuration file looks like this:
+
+{
+    "image_dim_ordering": "tf",
+    "epsilon": 1e-07,
+    "floatx": "float32",
+    "backend": "theano"
+}
+
+Simply change the field backend to either "theano" or  "tensorflow",
+and Keras will use the new configuration next time you run any Keras code.
+----------------------------
+Method 2:
+
+Call this line in terminal and change the BACKEND to tensorflow or theano:
+
+KERAS_BACKEND=tensorflow python -c "from keras import backend"
+
+---this is for python2+
+
+KERAS_BACKEND=tensorflow python3 -c "from keras import backend"
+
+---this is for python3+
+
+"""
+

kerasTUT/4-regressor_example.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 4 - Regressor example
+
+import numpy as np
+np.random.seed(1337)  # for reproducibility
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy as np
+import matplotlib.pyplot as plt
+
+# create some data
+X = np.linspace(-1, 1, 200)
+np.random.shuffle(X)    # randomize the data
+Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
+# plot data
+plt.scatter(X, Y)
+plt.show()
+
+X_train, Y_train = X[:160], Y[:160]     # first 160 data points
+X_test, Y_test = X[160:], Y[160:]       # last 40 data points
+
+# build a neural network from the 1st layer to the last layer
+model = Sequential()
+model.add(Dense(output_dim=1, input_dim=1))
+
+# choose loss function and optimizing method
+model.compile(loss='mse', optimizer='sgd')
+
+# training
+print('Training -----------')
+for step in range(301):
+    cost = model.train_on_batch(X_train, Y_train)
+    if step % 100 == 0:
+        print('train cost: ', cost)
+
+# test
+print('\nTesting ------------')
+cost = model.evaluate(X_test, Y_test, batch_size=40)
+print('test cost:', cost)
+W, b = model.layers[0].get_weights()
+print('Weights=', W, '\nbiases=', b)

kerasTUT/5-classifier_example.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 5 - Classifier example
+
+import numpy as np
+np.random.seed(1337)  # for reproducibility
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+from keras.optimizers import RMSprop
+
+# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
+# X shape (60,000 28x28), y shape (10,000, )
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# data pre-processing
+X_train = X_train.reshape(X_train.shape[0], -1) / 255   # normalize
+X_test = X_test.reshape(X_test.shape[0], -1) / 255      # normalize
+y_train = np_utils.to_categorical(y_train, nb_classes=10)
+y_test = np_utils.to_categorical(y_test, nb_classes=10)
+
+# Another way to build your neural net
+model = Sequential([
+    Dense(32, input_dim=784),
+    Activation('relu'),
+    Dense(10),
+    Activation('softmax'),
+])
+
+# Another way to define your optimizer
+rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
+
+# We add metrics to get more results you want to see
+model.compile(optimizer=rmsprop,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+print('Training ------------')
+# Another way to train the model
+model.fit(X_train, y_train, nb_epoch=2, batch_size=32)
+
+print('\nTesting ------------')
+# Evaluate the model with the metrics we defined earlier
+loss, accuracy = model.evaluate(X_test, y_test)
+
+print('test loss: ', loss)
+print('test accuracy: ', accuracy)
+
+

kerasTUT/6-CNN_example.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 6 - CNN example
+
+import numpy as np
+np.random.seed(1337)  # for reproducibility
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten
+from keras.optimizers import Adam
+
+# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
+# X shape (60,000 28x28), y shape (10,000, )
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# data pre-processing
+X_train = X_train.reshape(-1, 1, 28, 28)
+X_test = X_test.reshape(-1, 1, 28, 28)
+y_train = np_utils.to_categorical(y_train, nb_classes=10)
+y_test = np_utils.to_categorical(y_test, nb_classes=10)
+
+# Another way to build your CNN
+model = Sequential()
+
+# Conv layer 1 output shape (32, 28, 28)
+model.add(Convolution2D(
+    nb_filter=32,
+    nb_row=5,
+    nb_col=5,
+    border_mode='same',     # Padding method
+    input_shape=(1,         # channels
+                 28, 28)    # height & width
+))
+model.add(Activation('relu'))
+
+# Pooling layer 1 (max pooling) output shape (32, 14, 14)
+model.add(MaxPooling2D(
+    pool_size=(2, 2),
+    strides=(2, 2),
+    border_mode='same',    # Padding method
+))
+
+# Conv layer 2 output shape (64, 14, 14)
+model.add(Convolution2D(64, 5, 5, border_mode='same'))
+model.add(Activation('relu'))
+
+# Pooling layer 2 (max pooling) output shape (64, 7, 7)
+model.add(MaxPooling2D(pool_size=(2, 2), border_mode='same'))
+
+# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
+model.add(Flatten())
+model.add(Dense(1024))
+model.add(Activation('relu'))
+
+# Fully connected layer 2 to shape (10) for 10 classes
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+# Another way to define your optimizer
+adam = Adam(lr=1e-4)
+
+# We add metrics to get more results you want to see
+model.compile(optimizer=adam,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+print('Training ------------')
+# Another way to train the model
+model.fit(X_train, y_train, nb_epoch=1, batch_size=32,)
+
+print('\nTesting ------------')
+# Evaluate the model with the metrics we defined earlier
+loss, accuracy = model.evaluate(X_test, y_test)
+
+print('\ntest loss: ', loss)
+print('\ntest accuracy: ', accuracy)
+
+

kerasTUT/7-RNN_Classifier_example.py

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+"""
+To know more or get code samples, please visit my website:
+https://morvanzhou.github.io/tutorials/
+Or search: 莫烦Python
+Thank you for supporting!
+"""
+
+# please note, all tutorial code are running under python3.5.
+# If you use the version like python2.7, please modify the code accordingly
+
+# 8 - RNN Classifier example
+
+import numpy as np
+np.random.seed(1337)  # for reproducibility
+
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers import SimpleRNN, Activation, Dense
+from keras.optimizers import Adam
+
+TIME_STEPS = 28     # same as the height of the image
+INPUT_SIZE = 28     # same as the width of the image
+BATCH_SIZE = 50
+BATCH_INDEX = 0
+OUTPUT_SIZE = 10
+CELL_SIZE = 50
+LR = 0.001
+
+
+# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
+# X shape (60,000 28x28), y shape (10,000, )
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# data pre-processing
+X_train = X_train.reshape(-1, 28, 28) / 255      # normalize
+X_test = X_test.reshape(-1, 28, 28) / 255        # normalize
+y_train = np_utils.to_categorical(y_train, nb_classes=10)
+y_test = np_utils.to_categorical(y_test, nb_classes=10)
+
+# build RNN model
+model = Sequential()
+
+# RNN cell
+model.add(SimpleRNN(
+    batch_input_shape=(BATCH_SIZE, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
+    output_dim=CELL_SIZE,
+))
+
+# output layer
+model.add(Dense(OUTPUT_SIZE))
+model.add(Activation('softmax'))
+
+# optimizer
+adam = Adam(LR)
+model.compile(optimizer=adam,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# training
+for step in range(4001):
+    # data shape = (batch_num, steps, inputs/outputs)
+    X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
+    Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
+    cost = model.train_on_batch(X_batch, Y_batch)
+    BATCH_INDEX += BATCH_SIZE
+    BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX
+
+    if step % 500 == 0:
+        cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
+        print('test cost: ', cost, 'test accuracy: ', accuracy)
+
+
+
+