# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
total_samples_train = getNumSamples(variants[num_variant][0][0:4] + '.h5')
model.fit_generator(generate_arrays(TRAIN_SET,
batch_size=BATCH_SIZE,
max_sample=total_samples_train,
new_size=INPUT_FRAME_SIZE),
BATCH_SIZE,
EPOCHS,
verbose=2,
callbacks=[best_model],
validation_data=(x_test, y_test))
print("Finished fitting model")
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('All metrics', score)
x_train = HDF5Matrix(TRAIN_SET, 'data')
y_train = HDF5Matrix(TRAIN_SET, 'labels')
res = model.predict(x_test)
res_label = np.argmax(res, 1)
print('\ntest:', sum(res_label == y_t) / float(len(y_t)) * 100)
res = model.predict(x_train)
res_label = np.argmax(res, 1)
print('train:', sum(res_label == y_tr) / float(len(y_tr)) * 100)
lrate = initial_lrate * (drop ** np.floor(epoch/epochs_drop))
else:
lrate = initial_lrate * (drop ** np.floor((epoch-1)/epochs_drop))
print(lrate)
return lrate
lrate = LearningRateScheduler(step_decay)
Adam(lr=0.005, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0, amsgrad=False)
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
csv_logger = CSVLogger('./training_Xception.log', append=True)
checkpointer = ModelCheckpoint(filepath='./weights_Xception.hdf5', verbose=1, save_best_only=True, monitor = 'val_acc')
callbacks_list = [checkpointer,csv_logger,lrate]
model.fit_generator(traning_set, epochs=100, steps_per_epoch=len(x_train) / 50, validation_steps=len(x_valid) / 50, verbose=1, validation_data=validation_set,callbacks=callbacks_list)
score = model.evaluate(x_valid, y_valid, verbose=0)
print(score)
def main(args):
# hyper parameters
batch_size = 16
num_classes = 102
epochs = 100
# Instantiate model
model = Xception(include_top=True, weights=None, classes=num_classes)
# prepare data
x_train = np.load(os.path.join(current_directory, 'x_train.npy'))
y_train = np.load(os.path.join(current_directory, 'y_train.npy'))
x_test = np.load(os.path.join(current_directory, 'x_test.npy'))
y_test = np.load(os.path.join(current_directory, 'y_test.npy'))
# summary of the model
model.summary()
# compile model
model.compile(
loss=categorical_crossentropy,
optimizer=Adadelta(),
metrics=['accuracy']
)
# learning section
hist = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test)
)
# evaluation section
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# save graphs
acc = hist.history['acc']
val_acc = hist.history['val_acc']
loss = hist.history['loss']
val_loss = hist.history['val_loss']
plt.plot(range(epochs), loss, marker='.', label='acc')
plt.plot(range(epochs), val_loss, marker='.', label='val_acc')
plt.legend(loc='best')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('acc')
plt.savefig(os.path.join(current_directory, 'acc_xception.png'))
plt.clf()
plt.plot(range(epochs), acc, marker='.', label='loss')
plt.plot(range(epochs), val_acc, marker='.', label='val_loss')
plt.legend(loc='best')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig(os.path.join(current_directory, 'loss_xception.png'))
plt.clf()
lb = LabelBinarizer()
lb.fit(np.asarray(data['primary_microconstituent']))
y = lb.transform(labels)
print('\nLabels Binarized, converting array')
input = np.asarray(processed_imgs)
X_train, X_test, y_train, y_test = train_test_split(
input, y, test_size=0.1, random_state=42)
model = Xception(weights=None, classes = 7)
model.summary()
model.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy'])
model.fit(X_train, y_train, epochs = 5, batch_size = 32, validation_data=(X_test, y_test))
name = 'results/UHCS_Xception_Weights'
score = model.evaluate(X_test, y_test)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model.save_weights(name+'.h5')
file = open('Xception.txt', 'w')
file.write('Test score:', score[0])
file.write('Test accuracy:', score[1])
file.close()
Adam(lr=0.005,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
csv_logger = CSVLogger('./training_Xception_ten_res{}.log'.format(i),
append=True)
checkpointer = ModelCheckpoint(
filepath='./weights_Xception_ten_res{}.hdf5'.format(i),
verbose=1,
save_best_only=True,
monitor='val_acc')
callbacks_list = [checkpointer, csv_logger, lrate]
model.fit_generator(traning_set,
epochs=50,
steps_per_epoch=len(x_train) / 50,
verbose=1,
validation_data=validation_set,
validation_steps=len(x_valid) / 50,
callbacks=callbacks_list)
score.append(model.evaluate(x_valid, y_valid, verbose=0))
print(score[i])
