def train_xception(img_size=IMG_SIZE, batch_size=BATCH_SIZE):
xception_model = Xception(weights='imagenet', include_top=False)
xception_model.summary()
x = layers.GlobalAveragePooling2D()(xception_model.output)
x = layers.Flatten()(x)
x = layers.Dense(512)(x)
x = layers.Dense(512)(x)
x = layers.Dense(1000)(x)
model = tf.keras.Model(xception_model.input, x)
model.summary()
for layer in model.layers[:-3]:
layer.trainable = False
model = tf.keras.models.load_model('./Models/Xception')
train_df = pd.read_csv('./classes_train.csv')
validate_df = pd.read_csv('./classes_validate.csv')
model.compile(
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(beta_1=.999),
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_dataframe(
dataframe=train_df,
x_col='file',
y_col='class_num',
target_size=(img_size, img_size),
validate_filenames=False,
batch_size=batch_size,
class_mode='categorical')
validate_generator = train_datagen.flow_from_dataframe(
dataframe=validate_df,
x_col='file',
y_col='class_num',
validate_filenames=False,
target_size=(img_size, img_size),
batch_size=batch_size,
class_mode='categorical')
model.fit(train_generator,
validation_data=validate_generator,
epochs=10,
workers=16,
steps_per_epoch=VALID_SIZE // batch_size,
callbacks=[
tf.keras.callbacks.ModelCheckpoint('./Models/Xception'),
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
patience=2)
])
# print(model.evaluate(train_generator, steps=40, batch_size=batch_size*4))
model.save('./Models/Xception')
def train_model(self):
""" Training the model """
print("Training the model")
LR = 1e-3
epochs = 200
callbacks = [
EarlyStopping(monitor='val_loss',
min_delta=0,
patience=30,
verbose=0,
mode='auto'),
ModelCheckpoint('model.h5',
monitor='val_loss',
mode='min',
save_best_only=True),
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10,
verbose=0,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=0)
]
# Pre trained model Xception without fully connected layers
base_model = Xception(input_shape=(self.img_size[0], self.img_size[1],
3),
include_top=False,
weights='imagenet')
# Unfreeze the layers
base_model.trainable = True
x = GlobalMaxPooling2D()(base_model.output)
x = Dense(512, activation='relu')(x)
x = Dense(10, activation='relu')(x)
output = Dense(1, activation='linear')(x)
model = Model(inputs=base_model.input, outputs=output)
model.compile(loss='mse',
optimizer=Adam(learning_rate=LR),
metrics=[self.mae_in_months])
print(base_model.summary())
print(model.summary())
history = model.fit_generator(
self.train_datagen.flow(self.x_train,
self.y_train,
batch_size=self.batch_size),
steps_per_epoch=len(self.x_train) / self.batch_size,
validation_data=self.val_datagen.flow(self.x_val,
self.y_val,
batch_size=self.batch_size),
validation_steps=len(self.x_val) / self.batch_size,
callbacks=callbacks,
epochs=epochs,
verbose=1)
self.plot_it(history)
model.load_weights('model.h5')
pred = self.mean_bone_age + self.std_bone_age * (model.predict(
self.x_val, batch_size=self.batch_size, verbose=True))
actual = self.mean_bone_age + self.std_bone_age * (self.y_val)
def main():
xception_model = Xception(weights='imagenet',
input_shape=(IMG_SIZE, IMG_SIZE, 3),
include_top=False)
xception_model.summary()
x = layers.GlobalAveragePooling2D()(xception_model.output)
x = layers.Flatten()(x)
x = layers.Dense(512)(x)
x = layers.Dense(512)(x)
x = layers.Dense(1000)(x)
model = tf.keras.Model(xception_model.input, x)
model.summary()
train_df = pd.read_csv('./classes_train.csv')
validate_df = pd.read_csv('./classes_validate.csv')
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),
optimizer='adam',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_dataframe(
dataframe=train_df,
x_col='file',
y_col='class_num',
target_size=(IMG_SIZE, IMG_SIZE),
validate_filenames=False,
batch_size=BATCH_SIZE,
class_mode='categorical')
validate_generator = train_datagen.flow_from_dataframe(
dataframe=validate_df,
x_col='file',
y_col='class_num',
validate_filenames=False,
target_size=(IMG_SIZE, IMG_SIZE),
batch_size=BATCH_SIZE,
class_mode='categorical')
model.fit(train_generator,
validation_data=validate_generator,
epochs=10,
workers=16)
model.save('./Models/Xception')
def create_model(input_shape, config):
input_tensor = Input(
shape=input_shape) # this assumes K.image_dim_ordering() == 'tf'
xception_model = Xception(include_top=False,
weights=None,
input_tensor=input_tensor)
print(xception_model.summary())
x = xception_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(config["num_classes"], activation="softmax")(x)
return Model(input=xception_model.input, output=predictions)
def xception(weights='imagenet', include_top=False):
model = Xception(weights=weights, include_top=False)
print(model.summary())
return model
save_best_only=True,
mode='max')
# Log the epoch detail into csv
csv_logger = CSVLogger(os.path.join(output_folder, modelname + '.csv'))
callbacks_list = [checkpoint, csv_logger]
# define model
model = Xception(include_top=True,
input_shape=(IMG_SIZE, IMG_SIZE, 3),
weights=None,
classes=4)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
from tensorflow.keras.utils import plot_model
model_file = os.path.join(output_folder, modelname + "_model.png")
plot_model(model,
to_file=model_file,
show_shapes=True,
show_layer_names=False,
rankdir='TB')
# fit model
model.fit_generator(train_it,
validation_data=val_it,
epochs=50,
callbacks=callbacks_list)
#model.save(saved_model)
