def masks(batch_size):
batch_size += 64 - (batch_size % 64)
generator = GridGenerator()
artist = MaskGridArtist()
for masks in gen_grids.batches(batch_size, generator, artist=artist,
scales=[.25, .5, 1.]):
yield [m.astype(np.float64) for m in masks[:3]]
def train(model, weight_dir):
for i, (raw_grids, raw_labels) in enumerate(gen_grids.batches(batchsize=n_batch, generator=gen)):
print(i)
if i % 10 == 0:
file = os.path.abspath("{}/{:0>4}_all_degrees_model.hdf5".format(weight_dir, i))
print("saving weights to: " + file)
model.save_weights(file)
grids, labels = next(datagen.flow(raw_grids.astype(np.float32)/255, raw_labels, batch_size=n_batch))
model.fit(grids, labels, nb_epoch=1, batch_size=n_minibatch, verbose=1)
def test(model):
for i, (raw_grids, raw_labels) in enumerate(gen_grids.batches(batchsize=n_minibatch, generator=gen)):
grids, labels = next(datagen.flow(raw_grids.astype(np.float32)/255, raw_labels, batch_size=n_minibatch))
prediction = model.predict(grids, batch_size=n_minibatch, verbose=0)
bit_prediction = prediction > 0.5
n_right = 0.
n_total = labels.shape[0]
for i in range(n_total):
if (bit_prediction[i] == labels[i]).all():
n_right += 1
print(n_right / n_total)
model.add(Dense(2048, 512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512, 128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(128, NUM_CELLS))
model.add(Activation('sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=False)
grids, _ = next(gen_grids.batches(batchsize=2048, generator=gen))
grids = grids.astype(np.float32) / 255
datagen.fit(grids)
n_minibatch = 128
n_batch = n_minibatch * 12
weight_dir = "weights/all_degrees"
os.makedirs(weight_dir)
def train(model, weight_dir):
for i, (raw_grids, raw_labels) in enumerate(gen_grids.batches(batchsize=n_batch, generator=gen)):
print(i)
if i % 10 == 0:
file = os.path.abspath("{}/{:0>4}_all_degrees_model.hdf5".format(weight_dir, i))
print("saving weights to: " + file)
model.save_weights(file)