# In[20]:
import utils; reload(utils)
from utils import DataGenerator
NUM_TRAIN_PAIRS = 150000
NUM_VAL_PAIRS = 10000
BATCH_SIZE = 128
datagen = DataGenerator(X_train, y_train, num_train_pairs = NUM_TRAIN_PAIRS,
num_val_pairs = NUM_VAL_PAIRS, X_val = X_val[val_train],
train_alphabet_to_index = train_alphabet_to_index,
val_alphabet_to_index = val_train_index,
y_val = y_val[val_train], batch_sz = BATCH_SIZE, verbose = True)
datagen.create_data_transformer(rotation_range=10, width_shift_range=0.01,
height_shift_range=0.01, shear_range=0.01)
STEPS_PER_EPOCH = NUM_TRAIN_PAIRS // BATCH_SIZE
VALIDATION_STEPS = NUM_VAL_PAIRS // BATCH_SIZE
from keras.optimizers import Adam
learning_rate = 5e-5
adam = Adam(learning_rate)
scheduler = LearningRateScheduler(lambda epoch : learning_rate * pow(0.985, epoch))
siamese_net.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
siamese_net.load_weights(INIT_WEIGHTS)
# In[50]:
import utils; reload(utils)
from utils import TripletGenerator
NUM_TRAIN_TRIPLETS = 300000
NUM_VAL_TRIPLETS = 10000
BATCH_SIZE = 200
datagen = TripletGenerator(X_train, y_train, X_val, y_val, num_val_triplets=NUM_VAL_TRIPLETS,
batch_sz=BATCH_SIZE, num_train_triplets=NUM_TRAIN_TRIPLETS,
train_alphabet_to_index = train_alphabet_to_index,
test_alphabet_to_index = test_alphabet_to_index, random_transform=True)
# In[18]:
datagen.create_data_transformer()
# In[32]:
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5,
patience=3, verbose = 1, min_lr=1e-8)
early_stopping = EarlyStopping(monitor='oneshot_acc',
min_delta=1e-4,
patience=25,
verbose=0, mode='auto')
checkpointer = ModelCheckpoint(filepath=CHECKPOINTED_WEIGHTS, verbose=1, save_best_only=True, monitor='oneshot_acc')
