export const DUMMY_LAB = { name : 'Testlab' , description : 'Some Description' , tags : [ { name : 'MNIST' } , { name : 'NN' } ] , directory : [ { name : 'main.py' , content : `import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense

# the four different states of the XOR gate
training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")

# the four expected results in the same order
target_data = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(16, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='mean_squared_error',
            optimizer='adam',
            metrics=['binary_accuracy'])

model.fit(training_data, target_data, nb_epoch=500, verbose=2)

print model.predict(training_data).round()` } , { name : 'hello.py' , content : 'print "hello world";' } , { name : 'foo.py' , content : 'print "foo";' } , { name : 'bar.py' , content : 'print "bar";' } ] , status : 0 } ;