
Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
This cheat sheet contains basic example about Keras Data Sets
Typology: Cheat Sheet
1 / 1
This page cannot be seen from the preview
Don't miss anything!
Learn Python for Data Science Interactively
from keras.optimizers import RMSprop opt = RMSprop(lr=0.0001, decay=1e-6) (^) model2.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
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)
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
from keras.callbacks import EarlyStopping early_stopping_monitor = EarlyStopping(patience=2) model3.fit(x_train4, y_train4,
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])
from keras.models import Sequential model = Sequential() model2 = Sequential() model3 = Sequential()
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'))
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)
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
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'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
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'))
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)
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')
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))
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)
model3.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])