def train():
model = network.init_model(imggen.LABEL_SIZE, input_shape)
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
tensorboard = TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_images=True)
for epoch in range(0, iterations):
(x_train, y_train) = imggen.next_batch(TRAINING_SET_SIZE,
rotate=True,
ud=True,
multi_fonts=True,
multi_sizes=True,
blur=True)
model.fit(x_train,
y_train,
batch_size=32,
epochs=epoch + 1,
verbose=1,
validation_split=0.1,
callbacks=[tensorboard],
initial_epoch=epoch)
if not os.path.exists('results/data'):
os.makedirs('results/data')
model.save_weights('results/data/model%d.h5' % (epoch))
(x_test, y_test) = imggen.next_batch(TEST_SET_SIZE)
score = model.evaluate(x_test, y_test)
print "\n"
print 'Test score: {0:.4g}'.format(score[0])
print 'Test accur: {0:.4g}'.format(score[1])
def recognize(array):
path = os.getcwd()
array = 1 - array
global model
if model is None:
model = network.init_model(ig.LABEL_SIZE, train.input_shape)
model.load_weights(os.path.join(path, 'results/data/model.h5'))
pred = model.predict(array, batch_size=5, verbose=0)
return choose_char(pred[0], ig.chars)
def recognize(array):
array = 1 - array
global model
if model is None:
model = network.init_model(imggen.LABEL_SIZE, learning.input_shape)
model.load_weights('results/data/model.h5')
print model.predict(array, batch_size=1, verbose=0)
pred = model.predict_classes(array, batch_size=1, verbose=0)
ch = imggen.chars[pred[0]]
return ch
def train():
path = os.getcwd()
model = network.init_model(ig.LABEL_SIZE, input_shape)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
tensorboard = TensorBoard(log_dir=os.path.join(path, 'logs'),
histogram_freq=0,
write_graph=True,
write_images=True)
for epoch in range(0, iterations, nb_epoch):
(x_train, y_train) = ig.next_batch(TRAINING_SET_SIZE,
rotate=True,
ud=True,
lr=True,
multi_fonts=True,
multi_sizes=True,
blur=False)
model.fit(x_train,
y_train,
batch_size=32,
epochs=epoch + nb_epoch,
verbose=1,
validation_split=0.1,
callbacks=[tensorboard],
initial_epoch=epoch)
if not os.path.exists(os.path.join(path, 'results/data')):
os.makedirs(os.path.join(path, 'results/data'))
model.save_weights(os.path.join(path, 'results/data/model.h5'))
(x_test, y_test) = ig.next_batch(TEST_SET_SIZE)
score = model.evaluate(x_test, y_test)
tf.train.write_graph(K.get_session().graph, "results/data", "model.pb",
False)
print "\n"
print 'Test score: {0:.4g}'.format(score[0])
print 'Test accur: {0:.4g}'.format(score[1])
def recognize_chars(chars, image):
char_images = image_ops.crop_all_char_images(chars, image)
#print(char_images)
global model
path = os.getcwd()
if model is None:
model = network.init_model(ig.LABEL_SIZE, train.input_shape)
model.load_weights(os.path.join(path, 'results/data/model.h5'))
pred = model.predict(char_images, batch_size=len(chars), verbose=0)
#print choose_char(pred[0], ig.chars)[0]['char']
#print choose_char(pred[1], ig.chars)[0]['char']
chars = []
for p in pred:
chars.append(choose_char(p, ig.chars))
return chars
'beta1':0.533938,
'beta2':0.998234
}
baseline_params['batches_per_epoch'] = set_batches_per_epoch(baseline_params['batch_size'])
sherpa_algo = sherpa.algorithms.GPyOpt(max_num_trials=200)
study = sherpa.Study(parameters=params, algorithm=sherpa_algo, lower_is_better=True)
for trial in study:
trial_id = trial.id
savename = '{}/model/model.{}.h5'.format(algorithm, trial_id)
pars = deepcopy(trial.parameters)
for k,v in baseline_params.items():
pars[k] = v
model = init_model(baseline_params)
adam = init_adam(baseline_params)
datagen = init_datagen(pars)
model.compile(optimizer=adam, loss='categorical_crossentropy')
# Train but reserve the last little bit of data for
# a final testing set.
t_loss, v_loss = train(model, baseline_params, X_train, Y_train, X_test[:6000], Y_test[:6000],
patience=10, savename=savename, datagen=datagen)
# Log
study.add_observation(trial=trial, iteration=0, objective=min(v_loss))
# Save if it is not saved
if not os.path.exists(savename):
model.save(savename)
'max_epochs': 200,
}
sherpa_algo = sherpa.algorithms.GPyOpt(max_num_trials=80)
study = sherpa.Study(parameters=params,
algorithm=sherpa_algo,
lower_is_better=True)
for trial in study:
trial_id = trial.id
savename = '{}/model/model.{}.h5'.format(algorithm, trial_id)
pars = deepcopy(trial.parameters)
for k, v in baseline_params.items():
pars[k] = v
pars['batches_per_epoch'] = set_batches_per_epoch(pars['batch_size'])
model = init_model(pars)
adam = init_adam(pars)
datagen = init_datagen(pars)
model.compile(optimizer=adam, loss='categorical_crossentropy')
# Train but reserve the last little bit of data for
# a final testing set.
t_loss, v_loss = train(model,
pars,
X_train,
Y_train,
X_test[:6000],
Y_test[:6000],
patience=10,
savename=savename,
datagen=datagen)
'output_shape': Y_train.shape[1],
'depth': 3,
'dense_neurons': 512,
'init_filters': 20,
'use_batchnorm': True,
'dropout': 0.4,
'batch_size': 128,
'max_epochs': 5,
'learning_rate': 0.008,
'beta1': 0.5,
'beta2': 0.999
}
params['batches_per_epoch'] = set_batches_per_epoch(params['batch_size'])
print(params)
model = init_model(params)
adam = init_adam(params)
model.compile(optimizer=adam, loss='categorical_crossentropy')
print(model.summary())
# Train but reserve the last little bit of data for
# a final testing set.
t_loss, v_loss = train(model,
params,
X_train,
Y_train,
X_test[:6000],
Y_test[:6000],
patience=10,
savename=savename)