def demo():
X,y = utils.load_mnist()
y = utils.makeMultiClass(y)
# building the SDA
sDA = StackedDA([100])
# pre-trainning the SDA
sDA.pre_train(X[:100], noise_rate=0.3, epochs=1)
# saving a PNG representation of the first layer
W = sDA.Layers[0].W.T[:, 1:]
utils.saveTiles(W, img_shape= (28,28), tile_shape=(10,10), filename="results/res_dA.png")
# adding the final layer
sDA.finalLayer(X[:37500], y[:37500], epochs=2)
# trainning the whole network
sDA.fine_tune(X[:37500], y[:37500], epochs=2)
# predicting using the SDA
pred = sDA.predict(X[37500:]).argmax(1)
# let's see how the network did
y = y[37500:].argmax(1)
e = 0.0
for i in range(len(y)):
e += y[i]==pred[i]
# printing the result, this structure should result in 80% accuracy
print "accuracy: %2.2f%%"%(100*e/len(y))
return sDA
def demo(structure = [25**2, 23**2, 21**2,19**2,16**2, 15**2]):
# Getting the data
X,y = utils.load_mnist()
autoencoder = StackedDA([100], alpha=0.01)
autoencoder.pre_train(X[:1000], 10)
y = utils.makeMultiClass(y)
autoencoder.fine_tune(X[:1000], y[:1000], learning_layer=200, n_iters=20, alpha=0.01)
W = autoencoder.W[0].T[:, 1:]
W = utils.saveTiles(W, img_shape= (28,28), tile_shape=(10,10), filename="Results/res_dA.png")
def useLayers():
X,y = utils.load_mnist()
y = utils.makeMultiClass(y)
# Layers
sDA = StackedDA([100])
sDA.pre_train(X[:1000], rate=0.5, n_iters=500)
sDA.finalLayer(y[:1000], learner_size=200, n_iters=1)
sDA.fine_tune(X[:1000], y[:1000], n_iters=1)
pred = sDA.predict(X)
W = sDA.Layers[0].W.T[:, 1:]
W = utils.saveTiles(W, img_shape= (28,28), tile_shape=(10,10), filename="Results/res_dA.png")
return pred, y
def useLayers():
X, y = utils.load_mnist()
y = utils.makeMultiClass(y)
# Layers
sDA = StackedDA([100])
sDA.pre_train(X[:1000], rate=0.5, n_iters=500)
sDA.finalLayer(y[:1000], learner_size=200, n_iters=1)
sDA.fine_tune(X[:1000], y[:1000], n_iters=1)
pred = sDA.predict(X)
W = sDA.Layers[0].W.T[:, 1:]
W = utils.saveTiles(W,
img_shape=(28, 28),
tile_shape=(10, 10),
filename="Results/res_dA.png")
return pred, y
def demo(structure=[25**2, 23**2, 21**2, 19**2, 16**2, 15**2]):
# Getting the data
X, y = utils.load_mnist()
autoencoder = StackedDA([100], alpha=0.01)
autoencoder.pre_train(X[:1000], 10)
y = utils.makeMultiClass(y)
autoencoder.fine_tune(X[:1000],
y[:1000],
learning_layer=200,
n_iters=20,
alpha=0.01)
W = autoencoder.W[0].T[:, 1:]
W = utils.saveTiles(W,
img_shape=(28, 28),
tile_shape=(10, 10),
filename="Results/res_dA.png")
