def main():
batch_size = 32
num_classes = 4
epochs = 100
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'orientation-inception.h5'
data_train, data_test = load_data()
# Use Google Inception v3 model
model = InceptionV3(
include_top=False,
weights=None,
input_shape=(192, 192, 3),
pooling='softmax',
classes=4,
)
# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
checkpointer = ModelCheckpoint(
filepath=os.path.join(save_dir, 'checkpoint.h5'),
verbose=1,
save_best_only=True,
)
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
train_generator = DataGenerator(data_train)
val_generator = DataGenerator(data_test)
model.fit_generator(
train_generator.flow(batch_size=batch_size),
epochs=epochs,
validation_data=val_generator.flow(batch_size=batch_size),
shuffle=True,
callbacks=[checkpointer, early_stopping],
)
# Save model and weights
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s' % model_path)
# Score trained model.
scores = model.evaluate_generator(
val_generator.flow(batch_size=batch_size))
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
def main():
batch_size = 32
num_classes = 4
epochs = 100
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_orientation_trained_model.h5'
# (x_train, y_train), (x_test, y_test) = load_data()
# print('x_train shape:', x_train.shape)
# print(x_train.shape[0], 'train samples')
# print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
# y_train = keras.utils.to_categorical(y_train, num_classes)
# y_test = keras.utils.to_categorical(y_test, num_classes)
data_train, data_test = load_data2()
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(128, 128, 3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
checkpointer = ModelCheckpoint(filepath=os.path.join(
save_dir, 'checkpoint.hdf5'),
verbose=1,
save_best_only=True)
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
train_generator = DataGenerator(data_train)
val_generator = DataGenerator(data_test)
model.fit_generator(
train_generator.flow(batch_size=batch_size),
epochs=epochs,
validation_data=val_generator.flow(batch_size=batch_size),
shuffle=True,
callbacks=[checkpointer, early_stopping])
# Save model and weights
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s' % model_path)
# Score trained model.
scores = model.evaluate_generator(
val_generator.flow(batch_size=batch_size))
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])