def continue_training():
"""Continues training the chesspiece model based on SqueezeNet-v1.1.
"""
model = load_model("./models/SqueezeNet1p1.h5")
train_generator, validation_generator = data_generators(
preprocess_input, (227, 227), 64)
# Train all layers
for layer in model.layers:
layer.trainable = True
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy'])
callbacks = model_callbacks(20, "./models/SqueezeNet1p1_all.h5", 0.2, 8)
history = train_model(model, 100, train_generator, validation_generator,
callbacks, use_weights=False, workers=5)
plot_model_history(history, "./models/SqueezeNet1p1_all_acc.png",
"./models/SqueezeNet1p1_all_loss.png")
evaluate_model(model, validation_generator)
model.save("./models/SqueezeNet1p1_all_last.h5")
def continue_training():
"""Continues training the chesspiece model based on AlexNet."""
model = load_model("./models/AlexNet.h5")
train_generator, validation_generator = data_generators(
preprocess_input, (224, 224), 64)
model.compile(optimizer=Adam(lr=1e-4),
loss='categorical_crossentropy',
metrics=['accuracy'])
callbacks = model_callbacks(20, "./models/AlexNet_2.h5", 0.2, 8)
history = train_model(model,
100,
train_generator,
validation_generator,
callbacks,
use_weights=False,
workers=5)
plot_model_history(history, "./models/AlexNet_2_acc.png",
"./models/AlexNet_2_loss.png")
evaluate_model(model, validation_generator)
model.save("./models/AlexNet_2_last.h5")
def train_chesspiece_model():
"""Trains the chesspiece model based on MobileNetV2."""
base_model = MobileNetV2(input_shape=(224, 224, 3),
include_top=False,
weights='imagenet',
alpha=0.5)
# First train only the top layers
for layer in base_model.layers:
layer.trainable = False
model = build_model(base_model)
train_generator, validation_generator = data_generators(
preprocess_input, (224, 224), 64)
callbacks = model_callbacks(5, "./models/MobileNetV2_0p5_pre.h5", 0.1, 10)
history = train_model(model,
20,
train_generator,
validation_generator,
callbacks,
use_weights=False,
workers=10)
plot_model_history(history, "./models/MobileNetV2_0p5_pre_acc.png",
"./models/MobileNetV2_0p5_pre_loss.png")
evaluate_model(model, validation_generator)
# Also train blocks 14-16
for layer in model.layers[:126]:
layer.trainable = False
for layer in model.layers[126:]:
layer.trainable = True
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
callbacks = model_callbacks(20, "./models/MobileNetV2_0p5.h5", 0.2, 8)
history = train_model(model,
100,
train_generator,
validation_generator,
callbacks,
use_weights=False,
workers=10)
plot_model_history(history, "./models/MobileNetV2_0p5_acc.png",
"./models/MobileNetV2_0p5_loss.png")
evaluate_model(model, validation_generator)
model.save("./models/MobileNetV2_0p5_last.h5")
def train_chesspiece_model():
"""Trains the chesspiece model based on SqueezeNet-v1.1."""
base_model = SqueezeNet(input_shape=(227, 227, 3), include_top=False,
weights='imagenet')
# First train only the top layers
for layer in base_model.layers:
layer.trainable = False
model = build_model(base_model)
train_generator, validation_generator = data_generators(
preprocess_input, (227, 227), 64)
callbacks = model_callbacks(5, "./models/SqueezeNet1p1_pre.h5", 0.1, 10)
history = train_model(model, 20, train_generator, validation_generator,
callbacks, use_weights=False, workers=5)
plot_model_history(history, "./models/SqueezeNet1p1_pre_acc.png",
"./models/SqueezeNet1p1_pre_loss.png")
evaluate_model(model, validation_generator)
# Also train fire 7-9
for layer in model.layers[:41]:
layer.trainable = False
for layer in model.layers[41:]:
layer.trainable = True
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy'])
callbacks = model_callbacks(20, "./models/SqueezeNet1p1.h5", 0.2, 8)
history = train_model(model, 100, train_generator, validation_generator,
callbacks, use_weights=False, workers=5)
plot_model_history(history, "./models/SqueezeNet1p1_acc.png",
"./models/SqueezeNet1p1_loss.png")
evaluate_model(model, validation_generator)
model.save("./models/SqueezeNet1p1_last.h5")
def train_chesspiece_model():
"""Trains the chesspiece model based on AlexNet."""
model = alexnet(input_shape=(224, 224, 3))
train_generator, validation_generator = data_generators(
preprocess_input, (224, 224), 64)
callbacks = model_callbacks(20, "./models/AlexNet.h5", 0.2, 8)
history = train_model(model,
100,
train_generator,
validation_generator,
callbacks,
use_weights=False,
workers=5)
plot_model_history(history, "./models/AlexNet_acc.png",
"./models/AlexNet_loss.png")
evaluate_model(model, validation_generator)
model.save("./models/AlexNet_last.h5")