keras_example.py

from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

X, y = make_blobs(n_samples=4000, n_features=10)

# convert output classes (integers) to one hot vectors.
# this is necessary for keras. if you have 3 classes,
# for instance, keras expects y to be a matrix
# of nb_examples rows and 3 columns. 
# for each row, the column i (starting from zero) 
# is 1 if the class is i, otherwise it is 0
# for instance, the class 0 would be converted to
# [1 0 0], the class 1 to [0 1 0] and the class 2
# to [0 0 1]
y = to_categorical(y) 

# setup training, validation and test set
X_train, y_train = X[0:1000], y[0:1000]
X_valid, y_valid = X[1000:2000], y[1000:2000]
X_test, y_test = X[2000:], y[2000:]


# construct the architecture of the neural net
model = Sequential()
model.add(Dense(500, activation='relu', input_dim=10)) # 500 units in first layer, 10 is nb of input variables
model.add(Dense(500, activation='relu')) # 500 units in second layer
#model.add(Dropout(0.5)) #uncomment to apply dropout after the second layer
model.add(Dense(100, activation='relu')) # 100 units in third layer
#model.add(Dropout(0.5)) #uncomment to apply dropout after the third layer
model.add(Dense(3, activation='softmax')) # 3 units in output layer (nb of classes)
          
model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=Adadelta(lr=0.1),  # http://keras.io/optimizers/
              metrics=['accuracy'])

# train the model

batch_size = 128
nb_epoch = 20

model_filename = 'model.pkl' # save the model in this file

callbacks = [
    EarlyStopping(monitor='val_acc',
                  patience=10,
                  verbose=1,
                  mode='auto'),
    # this is used to save the model in a filename
    # it saves only the model at the epoch which gives
    # the best validation accuracy (because we use 'val_acc')
    ModelCheckpoint(model_filename, monitor='val_acc',
                    verbose=1,
                    save_best_only=True,
                    mode='auto'),
]
history = model.fit(X_train, y_train,
                    batch_size=batch_size, 
                    nb_epoch=nb_epoch,
                    callbacks=callbacks,
                    verbose=1, validation_data=(X_valid, y_valid))

model.load_weights(model_filename) # load the model in the epoch which gave the best validation accuracy

score = model.evaluate(X_test, y_test, verbose=0) # evaluate on test data
print('Test score:', score[0])
print('Test accuracy:', score[1])

# plot learning curves

plt.plot(history.history['acc'], label='train accuracy')
plt.plot(history.history['val_acc'], label='valid accuracy')
plt.legend()
plt.show()

plt.plot(history.history['loss'], label='train loss')
plt.plot(history.history['val_loss'], label='valid loss')
plt.legend()
plt.show()