Keras | Python For Data Science Cheat Sheet, Cheat Sheet of Data Structures and Algorithms

Download Keras | Python For Data Science Cheat Sheet and more Cheat Sheet Data Structures and Algorithms in PDF only on Docsity! Python For Data Science Cheat Sheet Keras Learn Python for data science Interactively at www.DataCamp.com Keras DataCamp Learn Python for Data Science Interactively Data Also see NumPy, Pandas & Scikit-Learn 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. Model Architecture 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']) 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) Preprocessing 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) Also see NumPy & Scikit-Learn >>> model.output_shape Model output shape >>> model.summary() Model summary representation >>> model.get_config() Model configuration >>> model.get_weights() List all weight tensors in the model Your data needs to be stored as NumPy arrays or as a list of NumPy arrays. Ide- ally, 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. 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]) Inspect Model Sequential Model >>> from keras.models import Sequential >>> model = Sequential() >>> model2 = Sequential() >>> model3 = Sequential() Multilayer Perceptron (MLP) >>> 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')) Standardization/Normalization Sequence Padding >>> from keras.preprocessing import sequence >>> x_train4 = sequence.pad_sequences(x_train4,maxlen=80) >>> x_test4 = sequence.pad_sequences(x_test4,maxlen=80) >>> from sklearn.preprocessing import StandardScaler >>> scaler = StandardScaler().fit(x_train2) >>> standardized_X = scaler.transform(x_train2) >>> standardized_X_test = scaler.transform(x_test2) 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 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) Compile Model 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']) >>> 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')) Prediction Evaluate Your Model's Performance >>> score = model3.evaluate(x_test, y_test, batch_size=32) >>> model3.predict(x_test4, batch_size=32) >>> model3.predict_classes(x_test4,batch_size=32) Model Training >>> model3.fit(x_train4, y_train4, batch_size=32, epochs=15, verbose=1, validation_data=(x_test4,y_test4)) >>> from keras.models import load_model >>> model3.save('model_file.h5') >>> my_model = load_model('my_model.h5') Save/ Reload Models >>> 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')) >>> model.add(Dense(64,activation='relu',input_dim=train_data.shape[1])) >>> model.add(Dense(1)) Binary Classification Multi-Class Classification Regression 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] >>> 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) Train and Test Sets Recurrent Neural Network >>> model3.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])