def main():
X_train, Y_train, X_test, Y_test = getKaggleMNIST()
X_train = X_train.astype(np.float32)
X_test = X_test.astype(np.float32)
_, D = X_train.shape
K = len(set(Y_train))
dnn = DNN(D, [1000, 750, 500], K, UnsupervisedModel=RBM)
init_op = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init_op)
dnn.set_session(session)
dnn.fit(X_train, Y_train, X_test, Y_test, pretrain=True, epochs=10)
def main():
Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
dnn = DNN([1000, 750, 500], UnsupervisedModel=RBM)
dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3)
def main():
Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
dnn = DNN([1000, 750, 500], UnsupervisedModel=RBM)
dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3)