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Keras for Data Science

Stephen Cheng

Intro

Keras is a powerful and easy-to-use deep learning library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.

A Basic Example:

>>> import numpy as np
>>> from keras.models import Sequential
>>> from keras.layers import Dense
>>> data = np.random.random((1000,100))
>>> labels = np.random.randint(2,size=(1000,1))
>>> model = Sequential()
>>> model.add(Dense(32,
 activation='relu',
 input_dim=100))
>>> model.add(Dense(1, activation='sigmoid'))
>>> model.compile(optimizer='rmsprop',
 loss='binary_crossentropy',
 metrics=['accuracy'])
>>> model.fit(data,labels,epochs=10,batch_size=32)
>>> predictions = model.predict(data)

Data

Your data needs to be stored as NumPy arrays or as a list of NumPy arrays. Ideally, you split the data in training and test sets, for which you can also resort to the train_test_split module of sklearn.cross_validation.

Keras Data Sets:

>>> from keras.datasets import boston_housing,
 mnist,
 cifar10,
 imdb
>>> (x_train,y_train),(x_test,y_test) = mnist.load_data()
>>> (x_train2,y_train2),(x_test2,y_test2) = boston_housing.load_data()
>>> (x_train3,y_train3),(x_test3,y_test3) = cifar10.load_data()
>>> (x_train4,y_train4),(x_test4,y_test4) = imdb.load_data(num_words=20000)
>>> num_classes = 10

Other:

>>> from urllib.request import urlopen
>>> data = np.loadtxt(urlopen("http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"),delimiter=",")
>>> X = data[:,0:8]
>>> y = data [:,8]

Preprocessing

Sequence Padding:

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>>> from keras.preprocessing import sequence
>>> x_train4 = sequence.pad_sequences(x_train4,maxlen=80)
>>> x_test4 = sequence.pad_sequences(x_test4,maxlen=80)

One-Hot Encoding:

>>> from keras.utils import to_categorical
>>> Y_train = to_categorical(y_train, num_classes)
>>> Y_test = to_categorical(y_test, num_classes)
>>> Y_train3 = to_categorical(y_train3, num_classes)
>>> Y_test3 = to_categorical(y_test3, num_classes)

Train and Test Sets:

1 2	>>> from sklearn.model_selection import train_test_split >>> X_train5,X_test5,y_train5,y_test5 = train_test_split(X, y, test_size=0.33, random_state=42)

Standardization/Normalization:

>>> from sklearn.preprocessing import StandardScaler
>>> scaler = StandardScaler().fit(x_train2)
>>> standardized_X = scaler.transform(x_train2)
>>> standardized_X_test = scaler.transform(x_test2)

Model Architecture

Sequential Model:

>>> from keras.models import Sequential
>>> model = Sequential()
>>> model2 = Sequential()
>>> model3 = Sequential()

Multilayer Perceptron (MLP):

Binary Classification

>>> from keras.layers import Dense
>>> model.add(Dense(12,
 input_dim=8,
 kernel_initializer='uniform',
 activation='relu'))
>>> model.add(Dense(8,kernel_initializer='uniform',activation='relu'))
>>> model.add(Dense(1,kernel_initializer='uniform',activation='sigmoid'))

Multi-Class Classification

>>> from keras.layers import Dropout
>>> model.add(Dense(512,activation='relu',input_shape=(784,)))
>>> model.add(Dropout(0.2))
>>> model.add(Dense(512,activation='relu'))
>>> model.add(Dropout(0.2))
>>> model.add(Dense(10,activation='softmax'))

Regression

1 2	>>> model.add(Dense(64,activation='relu',input_dim=train_data.shape[1])) >>> model.add(Dense(1))

Convolutional Neural Network (CNN):

>>> from keras.layers import Activation,Conv2D,MaxPooling2D,Flatten
>>> model2.add(Conv2D(32,(3,3),padding='same',input_shape=x_train.shape[1:]))
>>> model2.add(Activation('relu'))
>>> model2.add(Conv2D(32,(3,3)))
>>> model2.add(Activation('relu'))
>>> model2.add(MaxPooling2D(pool_size=(2,2)))
>>> model2.add(Dropout(0.25))
>>> model2.add(Conv2D(64,(3,3), padding='same'))
>>> model2.add(Activation('relu'))
>>> model2.add(Conv2D(64,(3, 3)))
>>> model2.add(Activation('relu'))
>>> model2.add(MaxPooling2D(pool_size=(2,2)))
>>> model2.add(Dropout(0.25))
>>> model2.add(Flatten())
>>> model2.add(Dense(512))
>>> model2.add(Activation('relu'))
>>> model2.add(Dropout(0.5))
>>> model2.add(Dense(num_classes))
>>> model2.add(Activation('softmax'))

Recurrent Neural Network (RNN):

>>> from keras.klayers import Embedding,LSTM
>>> model3.add(Embedding(20000,128))
>>> model3.add(LSTM(128,dropout=0.2,recurrent_dropout=0.2))
>>> model3.add(Dense(1,activation='sigmoid'))

Inspect Model

# Model output shape
>>> model.output_shape
# Model summary representation
>>> model.summary()
# Model configuration
>>> model.get_config()
# List all weight tensors in the model
>>> model.get_weights()

Compile Model

MLP: Binary Classification

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>>> model.compile(optimizer='adam',
 loss='binary_crossentropy',
 metrics=['accuracy'])

MLP: Multi-Class Classification

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>>> model.compile(optimizer='rmsprop',
 loss='categorical_crossentropy',
 metrics=['accuracy'])

MLP: Regression

1	>>> model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])

Recurrent Neural Network

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>>> model3.compile(loss='binary_crossentropy',
 optimizer='adam',
 metrics=['accuracy'])

Model Training

>>> model3.fit(x_train4,
 y_train4,
 batch_size=32,
 epochs=15,
 verbose=1,
 validation_data=(x_test4,y_test4))

Evaluate Your Model’s Performance

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>>> score = model3.evaluate(x_test,
 y_test,
 batch_size=32)

Prediction

1 2	>>> model3.predict(x_test4, batch_size=32) >>> model3.predict_classes(x_test4,batch_size=32)

Save/ Reload Models

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>>> from keras.models import load_model
>>> model3.save('model_file.h5')
>>> my_model = load_model('my_model.h5')

Model Fine-tuning

Optimization Parameters:

>>> from keras.optimizers import RMSprop
>>> opt = RMSprop(lr=0.0001, decay=1e-6)
>>> model2.compile(loss='categorical_crossentropy', optimizer=opt,
 metrics=['accuracy'])

Early Stopping:

>>> from keras.callbacks import EarlyStopping
>>> early_stopping_monitor = EarlyStopping(patience=2)
>>> model3.fit(x_train4,
 y_train4,
 batch_size=32,
 epochs=15,
 validation_data=(x_test4,y_test4),
 callbacks=[early_stopping_monitor])

Data-Science, Deep-Learning, Keras — Sep 21, 2019

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