def train():
current_path = os.getcwd()
data_path = current_path + "/dataset"
label_text = pd.read_csv((data_path + '/labels.csv'))
train_sub_path = current_path + '/train_sub/'
if not os.path.isdir(train_sub_path):
os.mkdir(train_sub_path)
for i in range(len(label_text)):
if os.path.exists(
(train_sub_path + label_text.loc[i]['breed'])) == False:
os.mkdir((train_sub_path + label_text.loc[i]['breed']))
shutil.copy(
(data_path + '/train/train/' + label_text.loc[i]['id'] + '.jpg'),
(train_sub_path + label_text.loc[i]['breed']))
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
#image generating
image_size = 299
batch_size = 32
train_datagen = ImageDataGenerator(rescale=1. / 255.,
horizontal_flip=True,
shear_range=0.2,
zoom_range=0.2,
width_shift_range=0.2,
height_shift_range=0.2,
validation_split=0.25)
valid_datagen = ImageDataGenerator(rescale=1. / 255.,
validation_split=0.25)
train_generator = train_datagen.flow_from_directory(
directory=train_sub_path,
subset="training",
batch_size=batch_size,
seed=42,
shuffle=True,
class_mode='categorical',
target_size=(image_size, image_size))
valid_generator = valid_datagen.flow_from_directory(
directory=train_sub_path,
subset='validation',
batch_size=1,
seed=42,
shuffle=True,
class_mode='categorical',
target_size=(image_size, image_size))
inceptionv3 = InceptionV3()
print(1)
for layer in inceptionv3.layers[:-5]:
layer.trainable = False
for layer in inceptionv3.layers[-5:]:
layer.trainable = True
print(2)
x = inceptionv3.layers[-2].output
predictions = tf.keras.layers.Dense(120, activation='softmax')(x)
model = tf.keras.Model(inputs=inceptionv3.input, outputs=predictions)
model.compile(optimizer='sgd',
loss='categorical_crossentropy',
metrics=['accuracy'])
print(5)
history = model.fit_generator(generator=train_generator,
steps_per_epoch=10,
epochs=50,
validation_data=valid_generator,
validation_steps=3)
print(3)
model.save('model.h5')
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], 'b-', label='loss')
plt.plot(history.history['val_loss'], 'r--', label='val_loss')
plt.xlabel('Epoch')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], 'g-', label='accuracy')
plt.plot(history.history['val_accuracy'], 'k--', label='val_accuracy')
plt.xlabel('Epoch')
plt.ylim(0, 0.1)
plt.show()