def fit_model(self, split_number):
self.log_event('Training with HyperParameter Tuning is started..')
tuner = RandomSearch(self.build_model,
objective=self.objective_function,
max_trials=self.max_trials,
executions_per_trial=self.execution_per_trial,
seed=self.random_state,
project_name='split_' + str(split_number),
directory=os.path.normpath(self.lstm_tuner_save_dir))
tuner.search(self.train_features, self.train_targets,
epochs=self.epochs,
batch_size=self.batch_size,
verbose=2,
validation_data=(self.test_features, self.test_targets))
model = tuner.get_best_models(num_models=1)[0]
print(model.summary())
keras.utils.plot_model(model,
to_file=self.lstm_model_save_dir + self.lstm_model_description + '.png',
show_shapes=True,
show_layer_names=True)
# Fit the best model of the split with the data
history = model.fit(x=self.train_features,
y=self.train_targets,
batch_size=self.batch_size,
epochs=self.epochs,
verbose=2,
validation_data=(self.test_features, self.test_targets))
# Save the model
current_time = datetime.datetime.now()
model.save(self.lstm_tuner_save_dir + '/split_' + str(split_number) + '/' + self.lstm_model_description + '_' + str(current_time.strftime('%Y-%m-%d_%H-%M-%S')) + '_' + '.h5')
self.lstm_model = model
self.model_history = history
hist_df = pd.DataFrame(history.history)
hist_df.to_csv(self.lstm_tuner_save_dir + '/split_' + str(split_number) + '/best_model_history.csv', index=False, header=True)
self.best_possible_models.append(hist_df)
self.print_summary(split_number)
self.log_event('Training with HyperParameter Tuning is finished..')
def tune_with_kerastuner1():
photos = load('dogs_vs_cats_photos.npy')
labels = load('dogs_vs_cats_labels.npy')
(trainX, testX, trainY, testY) = train_test_split(photos,
labels,
test_size=0.25,
random_state=42)
trainY = keras.utils.to_categorical(trainY, 2)
testY = keras.utils.to_categorical(testY, 2)
model = define_three_block_model()
# history = model.fit(photos, labels, batch_size=16, epochs=10, validation_split=0.33, verbose=1, use_multiprocessing=True)
tuner = RandomSearch(model,
objective='val_accuracy',
max_trials=5,
executions_per_trial=3,
directory="tuner_dir",
project_name="cats_vs_dogs_tuner")
tuner.search_space_summary()
# tuner.search(trainX, trainY,
# epochs=5,
# validation_data=(testX, testY))
models = tuner.get_best_models(num_models=2)
tuner.results_summary()
return tuner
def tune_with_kerastuner2():
# model = define_one_block_model()
tuner = RandomSearch(define_one_block_model,
objective='val_accuracy',
max_trials=5,
executions_per_trial=3,
directory="tuner_dir",
project_name="cats_vs_dogs_tuner")
train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
# width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
# prepare iterators
train_it = train_datagen.flow_from_directory('dataset_dogs_vs_cats/train/',
class_mode='binary',
batch_size=16,
target_size=(200, 200))
test_it = test_datagen.flow_from_directory('dataset_dogs_vs_cats/test/',
class_mode='binary',
batch_size=16,
target_size=(200, 200))
tuner.search(train_it,
steps_per_epoch=len(train_it),
validation_data=test_it,
validation_steps=len(test_it),
epochs=5,
use_multiprocessing=True)
models = tuner.get_best_models(num_models=3)
tuner.results_summary()
return tuner
def load_results():
tuner = RandomSearch(FuturePredictionModelHyperparameters(
window_size=720, num_features=5, future_steps=144),
objective='val_loss',
max_trials=100,
directory='test_dir')
tuner.reload()
best = tuner.oracle.get_best_trials(20)
best.sort(
key=lambda trial: trial.metrics.get_best_value('val_accuracy')
if not np.isnan(trial.metrics.get_best_value('val_loss')) else np.nan,
reverse=True)
best_accuracy = list(
map(
lambda trial: trial.metrics.get_best_value('val_accuracy')
if not np.isnan(trial.metrics.get_best_value('val_loss')) else np.
nan, best))
print(best_accuracy)
def tuning(self, method="random"):
if method == "random":
return RandomSearch(self.build_model,
objective='mae',
max_trials=5,
executions_per_trial=2,
directory='./tuning',
project_name='CNN2')
if method == "hyperband":
return Hyperband(self.build_model,
objective='mae',
max_epochs=100,
factor=2,
directory='./tuning',
project_name='CNN2_hyperband_huber')
def search(dt=600,
window_size=360,
future_steps=144,
epochs=50,
with_time=True,
batch_size=128,
max_trials=200):
bathroom1 = Dataset.parse('dataset/', 'bathroom1')
kitchen1 = Dataset.parse('dataset/', 'kitchen1')
combined1 = bathroom1.combine(kitchen1)
X, y = prepare_data_future_steps(combined1,
window_size=window_size,
dt=dt,
with_time=with_time,
future_steps=future_steps)
X_train = X[:-4 * (3600 // dt) * 24, :, :]
X_val = X[-4 * (3600 // dt) * 24:-2 * (3600 // dt) * 24, :, :]
X_test = X[-2 * (3600 // dt) * 24:, :, :]
# For now only sensor 24
y_train = y[:-4 * (3600 // dt) * 24, :, 0]
y_val = y[-4 * (3600 // dt) * 24:-2 * (3600 // dt) * 24, :, 0]
y_test = y[-2 * (3600 // dt) * 24:, :, 0]
tuner = RandomSearch(FuturePredictionModelHyperparameters(
window_size=window_size,
num_features=X.shape[2],
future_steps=future_steps),
objective='val_loss',
max_trials=max_trials,
directory='test_dir')
tuner.search_space_summary()
tuner.search(x=X_train,
y=y_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(X_val, y_val),
callbacks=[IsNanEarlyStopper(monitor='loss')])
tuner.results_summary()
def search_model():
data = get_data()
data_train, data_val_model, data_val_interpretation, data_test = get_train_val_test_splits(data)
train_features = np.load("train_features.npy")
valid_features = np.load("valid_features.npy")
train_y = data_train["outcome"].values
valid_y = data_val_model["outcome"].values
tuner = RandomSearch(
get_hp_model,
objective='val_accuracy',
max_trials=20,
executions_per_trial=1,
directory='test',
project_name='test')
tuner.search(train_features, y=train_y, batch_size=32, epochs=300,
validation_data=(valid_features, valid_y), verbose=2,
class_weight=dict(enumerate(utils.get_class_weights(train_y))),
# callbacks=[EarlyStopping(patience=30)]
)
tuner.results_summary()
model.add(Flatten())
model.add(
Dense(hp.Int('dense1', min_value=300, max_value=800, step=50),
activation="relu"))
model.add(Dense(2, activation="softmax"))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
tuner_search = RandomSearch(build_model,
objective='accuracy',
max_trials=5,
directory=r"C:\Users\khares\Work\output13")
tuner_search.search(training_set, epochs=5)
model = tuner_search.get_best_models(num_models=1)[0]
model.summary()
best_hyperparameters = tuner_search.get_best_hyperparameters(1)[0].values
best_hyperparameters
model = Sequential()
model.add(
Conv2D(filters=48,
kernel_size=5,
model.add(keras.layers.Dense(1, activation='sigmoid'))
hp_learning_rate = hp.Choice('learning_rate', values = [1e-2, 1e-3, 1e-4])
model.compile(optimizer = keras.optimizers.Adam(learning_rate = hp_learning_rate),
loss='binary_crossentropy',
metrics = ['accuracy'])
return model
!pip install keras.tuner
from tensorflow import keras
from kerastuner import RandomSearch
tuner_search=RandomSearch(model_builder,
objective = 'val_accuracy',
max_trials = 5,
directory = 'malaria_output',
project_name = 'Malaria_Detection')
tuner_search.search(x_train, y_train, epochs=3, validation_split=0.1)
model=tuner_search.get_best_models(num_models=1)[0]
model.summary()
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=3)
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=50, initial_epoch=3, callbacks=[early_stop])
model.save('malariadisease.h5')
metrics=['accuracy']
)
return model
def print_return(v):
print(v.shape)
return v
if __name__ == '__main__':
# tf.keras.backend.set_floatx('float16')
dt = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
hypermodel = HCNN()
train, test = get_mnist()
tuner = RandomSearch(
hypermodel,
objective='accuracy',
max_trials=40,
directory='models',
project_name='H-MNIST-' + dt
)
print('Created tuner')
log_dir = "logs/fit/" + dt
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
tuner.search(*train, epochs=30, validation_data=test, batch_size=32,
callbacks=[tensorboard_callback])
hypermodel = Hyperinceptiontime(input_shape=x_train.shape[1:],
classes=NUM_CLASSES)
# Initialize the hypertuner
# tuner = RandomSearch(
# hypermodel,
# objective='val_loss',
# max_trials=2,
# project_name='AF_inceptiontime',
# directory='test_directory')
tuner = RandomSearch(
hypermodel,
objective='val_loss',
max_trials=20,
# hyperband_iterations=20,
project_name='UCI_inceptiontime',
directory='nas_result')
# Display search overview.
tuner.search_space_summary()
# Performs the hypertuning.
tuner.search(x_train,
y_train,
epochs=100,
validation_split=0.1,
batch_size=128,
callbacks=[keras.callbacks.EarlyStopping(patience=10)])
def hyper_tuner(month_path, log_path, time_step, train_data, valid_data,
test_data, item, item_index, item_name):
train_x, valid_x, test_x, train_y, valid_y, test_y = data_split_norm(
train_data, valid_data, test_data, item_index, time_step)
hyper_model = MyHyperModel(input_shape=train_x.shape[-2:])
project_name = item_name + "_" + str(time_step)
tuner = RandomSearch(hyper_model,
objective='val_loss',
max_trials=150,
executions_per_trial=5,
directory=log_path,
project_name=project_name)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=15,
restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='auto',
min_delta=0.0001),
ClearTrainingOutput()
]
tuner.search_space_summary()
tuner.search(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
# Get the optimal hyperparameters
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)
history = model.fit(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
models_path = os.path.join(month_path, "models")
if not os.path.exists(models_path):
os.mkdir(models_path)
model_name = item + "_model.h5"
model_path = os.path.join(models_path, model_name)
model.save(model_path)
# 清除tuner
del tuner
layers_path = os.path.join(month_path, item)
if not os.path.exists(layers_path):
os.mkdir(layers_path)
plot_path_1 = os.path.join(layers_path, "loss_epoch.png")
plot_path_2 = os.path.join(layers_path, "measure_predict.png")
csv_path = os.path.join(layers_path, "measure_predict.csv")
history = history.history
plt.plot(history['loss'], linewidth=2, label='Train')
plt.plot(history['val_loss'], linewidth=2, label='Test')
plt.legend(loc='upper right')
plt.ylabel('Mean Relative Error [$' + item + '$]')
plt.xlabel('Epoch')
plt.savefig(plot_path_1, dpi=300, bbox_inches="tight")
plt.close()
test_predictions = model.predict(test_x)
plt.figure(figsize=(20, 6))
plt.plot(test_y, label='measure')
plt.plot(test_predictions, label='predict')
plt.legend(loc='upper right')
if item is 'PH':
plt.ylabel(item)
else:
plt.ylabel(item + '(mg/L)')
plt.xlabel('Test set')
plt.savefig(plot_path_2, dpi=300, bbox_inches="tight")
plt.close()
test_predictions = test_predictions.flatten() # flatten()降为一维
valid_predictions = model.predict(valid_x).flatten()
train_fvu, train_rmse, train_mre = history['loss'][-1], history[
'root_mean_squared_error'][-1], history[
"mean_absolute_percentage_error"][-1]
valid_fvu, valid_rmse, valid_mre = model.evaluate(valid_x,
valid_y,
verbose=0)
valid_nse, valid_cc = calc_nse_cc(valid_y, valid_predictions)
test_fvu, test_rmse, test_mre = model.evaluate(test_x, test_y, verbose=0)
test_nse, test_cc = calc_nse_cc(test_y, test_predictions)
measure_predict_data = pd.DataFrame()
measure_predict_data['predict'] = test_predictions
measure_predict_data['measure'] = test_y
measure_predict_data.to_csv(csv_path, encoding='utf-8')
try:
hidden_layer_1 = best_hps.get('units_0')
except:
hidden_layer_1 = 0
try:
hidden_layer_2 = best_hps.get('units_1')
except:
hidden_layer_2 = 0
model_info = {
'output': item,
"time_step": time_step,
'hidden_layer_1': hidden_layer_1,
'hidden_layer_2': hidden_layer_2,
'hidden_layer_3': best_hps.get('units_n'),
'train_fvu': train_fvu,
'train_rmse': train_rmse,
'train_mre': train_mre,
"valid_fvu": valid_fvu,
'valid_nse': valid_nse,
'valid_rmse': valid_rmse,
'valid_mre': valid_mre,
'valid_cc': valid_cc,
"test_fvu": test_fvu,
'test_nse': test_nse,
'test_rmse': test_rmse,
'test_mre': test_mre,
'test_cc': test_cc
}
return model_info
green = (0,255,0)
blue = (0,0,255)
black = (0,0,0)
white = (255,255,255)
pygame.init()
display=pygame.display.set_mode((display_width,display_height))
clock=pygame.time.Clock()
training_data_x, training_data_y = generate_training_data(display,clock)
"""Создание тюнера"""
tuner = RandomSearch(
build_model,
objective='val_accuracy',
max_trials=30,
directory='C:\\Users\\agolovanov\\PycharmProjects\\Snake_tuner\\model' # каталог, куда сохраняются обученные сети
)
tuner.search_space_summary()
tuner.search((np.array(training_data_x).reshape(-1,7)),
( np.array(training_data_y).reshape(-1,3)),
batch_size=2048,
epochs=7,
validation_split=0.2,
verbose=1
)
tuner.results_summary()
model.compile(optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', values=[1e-2, 1e-3])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
#!pip install keras-tuner
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
tuner = RandomSearch(build_model,
objective='val_accuracy',
max_trials=2,
directory='output',
project_name="CIFAR102")
# model.fit(X_Train ,y_train)
tuner.search(X_train, y_train, epochs=10, validation_split=0.2)
model = tuner.get_best_models(num_models=1)[0]
model.summary()
model.fit(X_train, y_train, epochs=10, validation_split=0.1)
model.evaluate(X_test, y_test)
#Predictions
keras.layers.Flatten(),
keras.layers.Dense(units=hp.Int('dense_1_units',
min_value=32,
max_value=128,
step=16),
activation='relu'),
keras.layers.Dense(units=10, activation='softmax')
])
model.compile(optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', values=[1e-2, 1e-3])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
model_tuning = RandomSearch(build_model,
objective='val_accuracy',
max_trials=5)
model_tuning.search(images_train, label_train, epochs=3, validation_split=0.2)
best_model = model_tuning.get_best_models(num_models=1)[0]
best_model.fit(images_train,
label_train,
epochs=10,
validation_split=0.2,
initial_epoch=3)
min_value=0.0,
max_value=0.5,
default=0.25,
step=0.05)))
model.add(Dense(4, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
LOG_DIR = f"{int(time.time())}"
tuner = RandomSearch(build_model,
objective="val_accuracy",
max_trials=1,
executions_per_trial=1,
directory=LOG_DIR)
tuner.search(x=X_train,
y=y_train,
epochs=150,
batch_size=64,
validation_split=0.1)
print(tuner.get_best_hyperparameters()[0].values)
print(tuner.results_summary())
best_model = tuner.get_best_models(num_models=1)[0]
NN_pred = best_model.predict(X_test)
pred = list()
for i in range(len(NN_pred)):
model.add(layers.Dropout(rate=0.5))
model.add(layers.Dense(6, activation='softmax'))
model.compile(optimizer=Optimizer.Adam(learning_rate=hp.Choice(
'learning_rate', values=[1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
# Initialize the model
hypermodel = RegressionHyperModel()
# Run the random search
tuner_rs = RandomSearch(hypermodel,
objective='val_accuracy',
max_trials=5,
executions_per_trial=1)
tuner_rs.search_space_summary()
tuner_rs.search(train_ds, train_classes, epochs=15, validation_split=0.30)
# Get the best model
best_model = tuner_rs.get_best_models(num_models=1)[0]
# Evaluate
val_ds, val_classes = getImages(
'../input/intel-image-classification/seg_test/seg_test/', 150)
best_model.evaluate(val_ds, val_classes, verbose=1)
filters=hp.Int('conv_2_filter', min_value=32, max_value=128, step=16),
kernel_size=hp.Choice('conv_2_kernel', values=[3, 5]),
activation='relu'
))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(
units=hp.Int('dense_1_units', min_value=32, max_value=128, step=16),
activation='relu'
))
model.add(keras.layers.Dense(10, activation="softmax")
)
model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
return model
tuner_search = RandomSearch(build_model, objective='val_accuracy', max_trials=3, directory='output',
project_name="Fashion_MNIST-Using-Keras_Tuner")
tuner_search.search(train_images, train_labels, epochs=3, validation_split=0.3)
best_model = tuner_search.get_best_models(num_models=1)[0]
best_model.summary()
best_model.fit(train_images, train_labels, epochs=5, validation_split=0.3, initial_epoch=3)
train_level, train_flow)
x_valid_simple_net, y_valid_simple_net = obj1.organized_simple_net_data(
valid_level, valid_flow)
x_test_simple_net, y_test_simple_net = obj1.organized_simple_net_data(
test_level, test_flow)
# -------------------------find hyper-parameters with keras tuner----------------------------
log_dir = os.path.normpath(
r'C:\Users\nirro\Desktop\machine learning\ayyeka\models\model_4')
input_shape = x_train_simple_net.shape[1]
hypermodel = RegressionHyperModel((input_shape, ))
tuner_rs = RandomSearch(hypermodel,
objective='mse',
seed=42,
max_trials=30,
executions_per_trial=2,
directory=log_dir)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
mode='min',
patience=100,
restore_best_weights=True,
min_delta=0.00001,
verbose=1)
]
tuner_rs.search(x_train_simple_net,
y_train_simple_net,
epochs=1000,
validation_split=0.2,
verbose=0,
def hyper_tuner(log_path, time_step, train_data, valid_data, test_data, item,
item_index, item_name):
keras.backend.clear_session()
train_x, valid_x, test_x, train_y, valid_y, test_y = data_split_norm(
train_data, valid_data, test_data, item_index, time_step)
hyper_model = MyHyperModel(input_shape=train_x.shape[-2:])
project_name = item_name + "_" + str(time_step)
tuner = RandomSearch(hyper_model,
objective='val_loss',
max_trials=150,
executions_per_trial=10,
directory=log_path,
project_name=project_name)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=15,
restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='auto',
min_delta=0.0001),
ClearTrainingOutput()
]
tuner.search_space_summary()
tuner.search(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
# Get the optimal hyperparameters
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
try:
hidden_layer_1 = best_hps.get('units_0')
except:
hidden_layer_1 = 0
try:
hidden_layer_2 = best_hps.get('units_1')
except:
hidden_layer_2 = 0
model_info = {
'output': item,
"time_step": time_step,
'hidden_layer_1': hidden_layer_1,
'hidden_layer_2': hidden_layer_2,
'hidden_layer_3': best_hps.get('units_n'),
}
# 清除tuner
del tuner
return model_info
activation='relu'
),
# in dense layer we can select how many no of nodes we can use between 32 -128. lot of permu and combi to select
keras.layers.Dense(10, activation='softmax')
# the last dense layer with 10 output nodes
])
model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
from kerastuner import RandomSearch # random search will try to find out which parameter will be best for problem statement
from kerastuner.engine.hyperparameters import HyperParameters
# run random search
# which conv layer and how many filters need to use
tuner_search = RandomSearch(build_model,objective='val_accuracy',max_trials=5,directory='CatsVsDogs',project_name='Mnist Fashion')
# will search for best parameters from the build_model
tuner_search.search(train_images,train_labels,epochs=3,validation_split=0.1) # will run only for 2 epochs by default. similar to fit
model = tuner_search.get_best_models(num_models=1)[0]
model.summary()
# considering the above model, we'll train the images
model.fit(train_images,train_labels,epochs=10,validation_split=0.1,initial_epoch=3)
keras.layers.Conv2D(
filters = hp.Int('conv_2_filter', min_value = 32, max_value = 256, step = 16),
kernel_size = hp.Choice('conv_2_kernel', values = [3, 5]),
activation = 'relu'
),
keras.layers.Flatten(),
keras.layers.Dense(
units = hp.Int('dense_1_units', min_value = 32, max_value = 256, step = 16),
activation = 'relu'
),
keras.layers.Dense(10, activation = 'softmax')
])
model.compile(optimizer = keras.optimizers.Adam(hp.Choice('learning_rate', values = [1e-2, 1e-3])),
loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
return model
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
tuner_search = RandomSearch(build_model, objective = 'val_accuracy', max_trials = 5,
directory = '/users/siddharthsmac/desktop', project_name = 'Mnist Fashion')
tuner_search.search(train_images, train_labels, epochs = 3, validation_split = 0.1)
model = tuner_search.get_best_models(num_models = 1)[0]
model.summary()
model.fit(train_images, train_labels, epochs = 10, validation_split = 0.1, initial_epoch = 3)
from kerastuner import RandomSearch
# Import the Cifar10 dataset.
NUM_CLASSES = 10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = to_categorical(y_train, NUM_CLASSES)
y_test = to_categorical(y_test, NUM_CLASSES)
# Import an hypertunable version of Xception.
hypermodel = HyperXception(input_shape=x_train.shape[1:], classes=NUM_CLASSES)
# Initialize the hypertuner: we should find the model that maximixes the
# validation accuracy, using 40 trials in total.
tuner = RandomSearch(hypermodel,
objective='val_accuracy',
max_trials=40,
project_name='cifar10_xception',
directory='test_directory')
# Display search overview.
tuner.search_space_summary()
# Performs the hypertuning.
tuner.search(x_train, y_train, epochs=10, validation_split=0.1)
# Show the best models, their hyperparameters, and the resulting metrics.
tuner.results_summary()
# Retrieve the best model.
best_model = tuner.get_best_models(num_models=1)[0]
),
keras.layers.Flatten(),
keras.layers.Dense(
units=hp.Int('dense_1_units', min_value=32, max_value=128, step=16),
activation='relu'
),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
tuner_search=RandomSearch(build_model,
objective='val_accuracy',
max_trials=5,directory='output',project_name="Mnist Fashion")
tuner_search.search(train_images,train_labels,epochs=3,validation_split=0.1)
model = tuner_search.get_best_models(num_models=1)[0]
model.summary()
model.fit(train_images,train_labels, epochs=10, validation_split=0.1, initial_epoch=3)
min_value=32,
max_value=128,
step=16),
kernel_size=hp.Int('Conv_2_Kernel', 3, 5),
activation='relu'),
keras.layers.Flatten(),
keras.layers.Dense(units=hp.Int('dense_units_1',
min_value=30,
max_value=128,
step=16),
activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer=keras.optimizers.Adam(
hp.Float('learning_rate', 0.1, 0.2)),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
tuner_search = RandomSearch(build_model,
objective='val_accuracy',
max_trials=5,
executions_per_trial=1,
directory='C:/',
project_name="mnist_fashion")
tuner_search.search(train_images,
data_set_train_label,
epochs=3,
validation_split=0.1,
batch_size=1000)
)
model.add(Dense(units=hp.Int('units_hidden', min_value=128, max_value=800, step=32), activation=activation_choice))
model.add(Dense(10, activation='softmax'))
model.compile(
optimizer=hp.Choice('optimizer', values=['adam', 'rmsprop', 'SGD']),
loss='categorical_crossentropy', metrics=['accuracy']
)
return model
tuner = RandomSearch(
build_model,
objective='val_accuracy',
max_trials=336,
directory='test_directory'
)
tuner.search_space_summary()
tuner.search(x_train, y_train, batch_size=256, epochs=70, validation_split=0.1)
tuner.results_summary()
models = tuner.get_best_models(num_models=3)
for m in models:
m.summary()
# m.evaluate(x_test, y_test)
# print()
x = layers.Flatten()(x)
outputs = layers.Dense(10, activation='softmax')(x)
# Build model
model = keras.Model(inputs, outputs)
model.compile(optimizer=Adam(lr),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
# Initialize the tuner by passing the `build_model` function
# and specifying key search constraints: maximize val_acc (objective),
# and the number of trials to do. More efficient tuners like UltraBand() can
# be used.
tuner = RandomSearch(build_model, objective='val_accuracy', max_trials=TRIALS,
project_name='hello_world_tutorial_results')
# Display search space overview
tuner.search_space_summary()
# Perform the model search. The search function has the same signature
# as `model.fit()`.
tuner.search(x_train, y_train, batch_size=128, epochs=EPOCHS,
validation_data=(x_val, y_val))
# Display the best models, their hyperparameters, and the resulting metrics.
tuner.results_summary()
# Retrieve the best model and display its architecture
best_model = tuner.get_best_models(num_models=1)[0]
best_model.summary()
#FC LAYER
add_fc_layer(model,dense_units = hp.Int('dense_1_units',min_value =32, max_value=128,step = 8), dropout_rate = 0.5)
#add_fc_layer(model,128,0.4)
add_fc_layer(model,dense_units = hp.Int('dense_2_units',min_value =32, max_value=128,step = 8),dropout_rate = 0.3)
#OUTPUT LAYER
model.add(Dense(units = 1, activation = 'sigmoid'))
model.compile(optimizer= 'Adam', loss= 'binary_crossentropy', metrics= ['accuracy'])
return model
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
tuner_search = RandomSearch(create_model,
objective='val_accuracy',
max_trials=5,directory=basepath,
project_name='Tuner_Folder')
tuner_search.search(train_X,train_y,epochs=3,validation_split=0.2)
model = tuner_search.get_best_models(num_models=1)[0]
model.summary()
model_history = model.fit(train_X,train_y,epochs=10, validation_split=0.1,initial_epoch=3,callbacks= [
tf.keras.callbacks.ModelCheckpoint('models/model_{val_accuracy:.3f}.h5',
save_best_only = True,save_weights_only = False,
monitor = 'val_accuracy')
])
model.save('model_final.h5')
model.add(Dropout(.3))
model.add(Dense(units=num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=Adam(
hp.Choice('learning_rate', values=[1e-4, 1e-3])),
metrics=['accuracy'])
return model
from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
tuner = RandomSearch(build_model,
objective='val_accuracy',
max_trials=5,
directory='output1',
project_name='EMNIST_Balanced_Tuned')
tuner.search(X_train, y_train, epochs=3, validation_data=(X_val, y_val))
model = tuner.get_best_models(num_models=1)[0]
# model.summary()
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
model = tuner.get_best_models(num_models=1)[0]
# Some callbacks that we're using
MCP = ModelCheckpoint('Best_points.h5',
verbose=1,
set_df_values(df)
df = clean_data(df)
print(df.dtypes)
ohe_cols = cols[1:36]
# print(ohe_cols)
df = one_hot_encode(df, colnames=ohe_cols)
x_train, x_val, y_train, y_val, train_ids, val_ids = split_dataset(
df, test_size=0.1, seed=42)
# X_train, Y_train = np.array(x_train), np.array(y_train)
# X_val, Y_val = np.array(x_val), np.array(y_val)
model = get_model(input_size=118, output_size=2, magic='tanh', dropout=0.5)
tuner = RandomSearch(get_tuned_model,
objective='val_binary_accuracy',
max_trials=7,
executions_per_trial=4,
directory='project',
project_name='Air Quality Index')
print(tuner.search_space_summary())
x_train = np.asarray(x_train).astype(np.float32)
y_train = np.asarray(y_train).astype(np.float32)
x_val = np.asarray(x_val).astype(np.float32)
y_val = np.asarray(y_val).astype(np.float32)
tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
print(tuner.results_summary(num_trials=3)) # 3 best models
test_acc, test_loss = fit_and_evaluate(model,
