Keras官方出调参工具了，然而Francois说先别急着用

from tensorflow import kerasfrom tensorflow.keras import layers
import numpy as np
from kerastuner.tuners import GridSearchfrom kerastuner.distributions import Range, Choice
(x, y), (val_x, val_y) = keras.datasets.mnist.load_data()x = x.astype('float32') / 255.val_x = val_x.astype('float32') / 255.

"""Basic case:- We define a `build_model` function- It returns a compiled model- It uses hyperparameters defined on the fly"""

def build_model(): model = keras.Sequential() model.add(layers.Flatten(input_shape=(28, 28))) for i in range(Range('num_layers', 2, 20)): model.add(layers.Dense(units=Range('units_' + str(i), 32, 512, 32), activation='relu')) model.add(layers.Dense(10, activation='softmax')) model.compile( optimizer=keras.optimizers.Adam( Choice('learning_rate', [1e-2, 1e-3, 1e-4])), loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model

tuner = GridSearch( build_model, objective='val_accuracy', num_executions=2)
tuner.search(x=x, y=y, validation_data=(val_x, val_y))

from tensorflow import kerasfrom tensorflow.keras import layersimport numpy as npfrom kerastuner.tuner import SequentialRandomSearch
(x, y), (val_x, val_y) = keras.datasets.mnist.load_data()x = x.astype('float32') / 255.val_x = val_x.astype('float32') / 255.

"""Basic case:- We define a `build_model` function-It returns a compiled model-It uses hyperparameters defined on the fly"""

def build_model(hp): model = keras.Sequential() model.add(layers.Flatten(input_shape=(28, 28))) for i in range(hp.Range('num_layers', 2, 20)): model.add(layers.Dense(units=hp.Range('units_' + str(i), 32, 512, 32), activation='relu')) model.add(layers.Dense(10, activation='softmax')) model.compile( optimizer=keras.optimizers.Adam( hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),  loss='sparse_categorical_crossentropy',  metrics=['accuracy']) return model

tuner = SequentialRandomSearch( build_model,  objective='val_accuracy')

tuner.search(trials=2,  x=x,  y=y,  epochs=5, ?validation_data=(val_x,?val_y))