已训练模型的超参数优化

Question

我有一个语料库，我把它分成了 3 个部分。

训练集80%
开发集 10%
测试集10%

我在训练集上训练了以下 CNN 模型并针对开发集进行了评估

model.add(SpatialDropout1D(0.1))
model.add(Conv1D(128, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.25))
model.add(Conv1D(64, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())

# Fully connected (Dense layer)
model.add(Dense(64, activation='relu'))

# Output layer with sigmoid activation function
model.add(Dense(8, activation='sigmoid'))

我使用 model.save('model.h5')

保存了这个模型

现在，我想对保存的训练模型进行超参数优化，提供我的开发集作为训练集和测试集进行评估。

我的价值观是

过滤器 32/64/128/192/256/512 128/64
内核大小 2/3/4/5/7 3
辍学率 0.1/0.2/0.3/0.4/0.5 0.1/0.25
密集层大小 16/32/64/128/256 32
批量大小 32/50/64/100 32
学习率 0.1/0.01/0.001

任何指示如何使用任何库（如 Talos）加载现有模型来实现此目的？

Answer 1

根据您的最后一条评论，来自 Keras documentation：

（寻找 "grid"，scikit-learn 网格搜索超参数微调。下面的代码应该完全运行原样。您可以对 saved/loaded 模型，使用你想要的数据集）

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.wrappers.scikit_learn import KerasClassifier
from keras import backend as K
from sklearn.model_selection import GridSearchCV


num_classes = 10

# input image dimensions
img_rows, img_cols = 28, 28

# load training data and do basic data normalization
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


def make_model(dense_layer_sizes, filters, kernel_size, pool_size):
    '''Creates model comprised of 2 convolutional layers followed by dense layers

    dense_layer_sizes: List of layer sizes.
        This list has one number for each layer
    filters: Number of convolutional filters in each convolutional layer
    kernel_size: Convolutional kernel size
    pool_size: Size of pooling area for max pooling
    '''

    model = Sequential()
    model.add(Conv2D(filters, kernel_size,
                     padding='valid',
                     input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Conv2D(filters, kernel_size))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=pool_size))
    model.add(Dropout(0.25))

    model.add(Flatten())
    for layer_size in dense_layer_sizes:
        model.add(Dense(layer_size))
        model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer='adadelta',
                  metrics=['accuracy'])
    return model


dense_size_candidates = [[32], [64], [32, 32], [64, 64]]
my_classifier = KerasClassifier(make_model, batch_size=32)
validator = GridSearchCV(my_classifier,
                         param_grid={'dense_layer_sizes': dense_size_candidates,
                                     # epochs is avail for tuning even when not
                                     # an argument to model building function
                                     'epochs': [3, 6],
                                     'filters': [8],
                                     'kernel_size': [3],
                                     'pool_size': [2]},
                         scoring='neg_log_loss',
                         n_jobs=1)
validator.fit(x_train, y_train)

print('The parameters of the best model are: ')
print(validator.best_params_)

# validator.best_estimator_ returns sklearn-wrapped version of best model.
# validator.best_estimator_.model returns the (unwrapped) keras model
best_model = validator.best_estimator_.model
metric_names = best_model.metrics_names
metric_values = best_model.evaluate(x_test, y_test)
for metric, value in zip(metric_names, metric_values):
    print(metric, ': ', value)

已训练模型的超参数优化

Hyperpameter optimization of already trained model

python

optimization

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