readme

1.Install Keras

git clone git://github.com/fchollet/keras.git

cd keras

python setup.py develop

2.Demo training data on keras with gpu

#### Create file 'test.py' and copy and paste this code #######

python test.py

test.py

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
 
data_dim = 100
nb_classes = 10
 
model = Sequential()
 
model.add(Dense(32, input_dim=data_dim,init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(64, input_dim=data_dim, init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
 
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',  
              metrics=["accuracy"])
 
# generate dummy training data
x_train = np.random.random((1000, data_dim))
y_train = np.random.random((1000, nb_classes))
 
# generate dummy test data
x_test = np.random.random((100, data_dim))
y_test = np.random.random((100, nb_classes))
 
model.fit(x_train, y_train,
          nb_epoch=50,
          batch_size=128)
 
score = model.evaluate(x_test, y_test, batch_size=16)