def predict(fname_specialists='net2.pickle'):
with open(fname_specialists, 'rb') as f:
net = pickle.load(f)
X = load2d(test=True)[0]
y_pred = net.predict(X)
y_pred2 = y_pred * 48 + 48
y_pred2 = y_pred2.clip(0, 96)
df = DataFrame(y_pred2)
lookup_table = read_csv(os.path.expanduser(FLOOKUP))
values = []
for index, row in lookup_table.iterrows():
values.append((
row.RowId,
y_pred2[int(row.ImageId)-1][int(row.RowId)%30-1]
))
submission = DataFrame(values, columns=('RowId','Location'))
filename = 'submission1.csv'
submission.to_csv(filename, index=False)
print("Wrote {}".format(filename))
#('conv3', layers.Conv2DLayer),
#('pool3', layers.MaxPool2DLayer),
('hidden4', layers.DenseLayer),
# ('hidden5', layers.DenseLayer),
('output', layers.DenseLayer),
],
input_shape=(None, 1, 96, 96),
conv1_num_filters=32, conv1_filter_size=(3, 3), pool1_pool_size=(2, 2),
conv2_num_filters=64, conv2_filter_size=(2, 2), pool2_pool_size=(2, 2),
#conv3_num_filters=128, conv3_filter_size=(2, 2), pool3_pool_size=(2, 2),
hidden4_num_units=500,
#hidden5_num_units=500,
output_num_units=30, output_nonlinearity=None,
update_learning_rate=0.01,
update_momentum=0.9,
regression=True,
max_epochs=30,
verbose=1,
)
X, y = load2d() # load 2-d data
net2.fit(X, y)
with open('net2.pickle', 'wb') as f:
pickle.dump(net2, f, -1)
FLOOKUP = 'IdLookupTable.csv'
def plot_sample(x, y, axis,y_true=None):
img = x.reshape(96, 96)
axis.imshow(img, cmap='gray')
axis.scatter(y[0::2] * 48 + 48, y[1::2] * 48 + 48, c='b',marker='x', s=10)
if y_true !=None:
axis.scatter(y_true[0::2] * 48 + 48, y_true[1::2] * 48 + 48,c='r', marker='x', s=10)
f1 = open('net1.pickle', 'r')
f2 = open('net2.pickle', 'r')
net1 = pickle.load(f1)
net2 = pickle.load(f2)
X1,y1 = load()
X2,y2 = load2d()
#X2 = load2d(test=True)
sample1,y_true1 = X1[6:7],y1[6]
sample2,y_true2 = X2[6:7],y2[6]
y_pred1 = net1.predict(sample1)[0]
y_pred2 = net2.predict(sample2)[0]
fig = plt.figure(figsize=(6, 3))
ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[])
plot_sample(sample1[0], y_pred1, ax,y_true1)
ax = fig.add_subplot(1, 2, 2, xticks=[], yticks=[])