Keras Cheat Sheet: Neural Networks in Python

Python For Data Science Cheat Sheet: Keras

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 tensorflow.keras.models import Sequential
>>> from tensorflow.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 tensorflow.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

>>> from tensorflow.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 tensorflow.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

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

Multi-Layer Perceptron (MLP)

Binary Classification

>>> from tensorflow.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 tensorflow.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

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

Convolutional Neural Network (CNN)

>>> from tensorflow.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 tensorflow.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

Multi-Layer Perceptron (MLP)

MLP: Binary Classification

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

MLP: Multi-Class Classification

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

MLP: Regression

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

Recurrent Neural Network (RNN)

>>> 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

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

Prediction

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

Save/Reload Models

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

Model Fine-Tuning

Optimization Parameters

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

Early Stopping

>>> from tensorflow.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])

Going Further

Begin with our Keras tutorial for beginners, in which you'll learn in an easy, step-by-step way how to explore and preprocess the wine quality data set, build up a multi-layer perceptron for classification and regression tasks, compile, fit and evaluate the model and fine-tune the model that you have built.

Also, don't miss out on our Scikit-Learn cheat sheet, NumPy cheat sheet and Pandas cheat sheet!