def main():
training_verbosity = 2
# Download data if needed and import.
mnist_data = mnist.input_data.read_data_sets('MNIST_data',
one_hot=True,
reshape=False)
val_images = mnist_data.validation.images
val_labels = mnist_data.validation.labels
# Create LeNet model
model = Sequential()
model.add(
Conv2D(20, [3, 3],
input_shape=[28, 28, 1],
activation='relu',
name='conv_1'))
model.add(MaxPool2D())
model.add(Conv2D(50, [3, 3], activation='relu', name='conv_2'))
model.add(MaxPool2D())
model.add(layers.Permute((2, 1, 3)))
model.add(Flatten())
model.add(Dense(500, activation='relu', name='dense_1'))
model.add(Dense(10, activation='softmax', name='dense_2'))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
early_stopping = callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=training_verbosity,
mode='auto')
reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=training_verbosity,
mode='auto',
epsilon=0.0001,
cooldown=0,
min_lr=0)
# Train LeNet on MNIST
results = model.fit(mnist_data.train.images,
mnist_data.train.labels,
epochs=200,
batch_size=128,
verbose=2,
validation_data=(val_images, val_labels),
callbacks=[early_stopping, reduce_lr])
loss = model.evaluate(val_images, val_labels, batch_size=128, verbose=2)
print('original model loss:', loss, '\n')
layer_name = 'dense_1'
while True:
layer = model.get_layer(name=layer_name)
apoz = identify.get_apoz(model, layer, val_images)
high_apoz_channels = identify.high_apoz(apoz)
model = delete_channels(model, layer, high_apoz_channels)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
loss = model.evaluate(val_images,
val_labels,
batch_size=128,
verbose=2)
print('model loss after pruning: ', loss, '\n')
results = model.fit(mnist_data.train.images,
mnist_data.train.labels,
epochs=200,
batch_size=128,
verbose=training_verbosity,
validation_data=(val_images, val_labels),
callbacks=[early_stopping, reduce_lr])
loss = model.evaluate(val_images,
val_labels,
batch_size=128,
verbose=2)
print('model loss after retraining: ', loss, '\n')
def main():
training_verbosity = 2
# Download data if needed and import.
mnist = input_data.read_data_sets("tempData", one_hot=True, reshape=False)
val_images = mnist.validation.images
val_labels = mnist.validation.labels
# Create LeNet model
model = Sequential()
model.add(
Conv2D(20, [3, 3],
input_shape=[28, 28, 1],
activation="relu",
name="conv_1"))
model.add(MaxPool2D())
model.add(Conv2D(50, [3, 3], activation="relu", name="conv_2"))
model.add(MaxPool2D())
model.add(layers.Permute((2, 1, 3)))
model.add(Flatten())
model.add(Dense(500, activation="relu", name="dense_1"))
model.add(Dense(10, activation="softmax", name="dense_2"))
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
early_stopping = callbacks.EarlyStopping(
monitor="val_loss",
min_delta=0,
patience=10,
verbose=training_verbosity,
mode="auto",
)
reduce_lr = callbacks.ReduceLROnPlateau(
monitor="val_loss",
factor=0.1,
patience=5,
verbose=training_verbosity,
mode="auto",
epsilon=0.0001,
cooldown=0,
min_lr=0,
)
# Train LeNet on MNIST
results = model.fit(
mnist.train.images,
mnist.train.labels,
epochs=200,
batch_size=128,
verbose=2,
validation_data=(val_images, val_labels),
callbacks=[early_stopping, reduce_lr],
)
loss = model.evaluate(val_images, val_labels, batch_size=128, verbose=2)
print("original model loss:", loss, "\n")
layer_name = "dense_1"
while True:
layer = model.get_layer(name=layer_name)
apoz = identify.get_apoz(model, layer, val_images)
high_apoz_channels = identify.high_apoz(apoz)
model = delete_channels(model, layer, high_apoz_channels)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
loss = model.evaluate(val_images,
val_labels,
batch_size=128,
verbose=2)
print("model loss after pruning: ", loss, "\n")
results = model.fit(
mnist.train.images,
mnist.train.labels,
epochs=200,
batch_size=128,
verbose=training_verbosity,
validation_data=(val_images, val_labels),
callbacks=[early_stopping, reduce_lr],
)
loss = model.evaluate(val_images,
val_labels,
batch_size=128,
verbose=2)
print("model loss after retraining: ", loss, "\n")
# Finally, evaluate our algorithm for 5 episodes.
cem.test(env, nb_episodes=5, visualize=True)
#observations = np.array((4,10))
observations = []
for _ in range(10):
observations.append(deepcopy(env.reset()))
observations = np.asarray(observations)
for layer in cem.model.layers:
fake_x_test = np.ndarray(shape=(10,1), dtype=float, order='F')
# apoz = identify.get_apoz(model, layer, x_test)
#apoz = identify.get_apoz(model, layer, observations)
apoz = identify.get_apoz(model, layer, env.reset())
high_apoz_channels = identify.high_apoz(apoz)
cem.model = delete_channels(cem.model, layer, high_apoz_channels)
print('layer name: ', layer.name)
cem.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
reward = cem.test(env, nb_episodes=5, visualize=True)
print('model loss after pruning: ', reward, '\n')
results = cem.fit(env, nb_steps=100000, visualize=True, verbose=2)
loss = cem.test(env, nb_episodes=5, visualize=True)
print('\033[93m','model loss after retraining: ', loss, '\033[0m','\n')