classifier_example.py
""" 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, num_classes=10) y_test = np_utils.to_categorical(y_test, num_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, epochs=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)