一、加载.h5文件
from keras.models import load_model model = load_model('***.h5') model.summary二、keras functional API 简介
from keras.models import Sequential, Model from keras import layers from keras import Input
#sequenti model seq_model = Sequential() seq_model.add(layers.Dense(32, activation='relu', input_shape=(64,))) seq_model.add(layers.Dense(32, activation='relu')) seq_model.add(layers.Dense(10, activation='softmax'))
#API input_tensor = Input(shape=(64,)) x = layers.Dense(32, activation='relu')(input_tensor) x = layers.Dense(32, activation='relu')(x) output_tensor = layers.Dense(10, activation='softmax')(x) model = Model(input_tensor, output_tensor) model.summary()
三、多输入模型
①两输入的问答模型的functional API 的应用
from keras.models import Model from keras import layers from keras import Input text_vocabulary_size = 10000 question_vocabulary_size = 10000 answer_vocabulary_size = 500 text_input = Input(shape=(None,), dtype='int32', name='text') embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input) encoded_text = layers.LSTM(32)(embedded_text) question_input = Input(shape=(None,),dtype='int32', name='question') embedded_question = layers.Embedding(32, question_vocabulary_size)(question_input) encoded_question = layers.LSTM(16)(embedded_question) concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1) answer = layers.Dense(answer_vocabulary_size, activation='softmax')(concatenated) model = Model([text_input, question_input], answer) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])
②喂数据
import numpy as np num_samples = 1000 max_length = 100 text = np.random.randint(1, text_vocabulary_size, size=(num_samples, max_length)) question = np.random.randint(1, question_vocabulary_size, size=(num_samples, max_length)) answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size))
# a list of inputs model.fit([text, question], answers, epochs=10, batch_size=128)
# a dictionary of inputs model.fit({'text': text, 'question': question}, answers, epochs=10, batch_size=128)四、多输出模型
①三输出模型的functional API 的应用
from keras import layers from keras import Input from keras.models import Model vocabulary_size = 50000 num_income_groups = 10 posts_input = Input(shape=(None,), dtype='int32', name='posts') embedded_posts = layers.Embedding(256, vocabulary_size)(posts_input) x = layers.Conv1D(128, 5, activation='relu')(embedded_posts) x = layers.MaxPooling1D(5)(x) x = layers.Conv1D(256, 5, activation='relu')(x) x = layers.Conv1D(256, 5, activation='relu')(x) x = layers.MaxPooling1D(5)(x) x = layers.Conv1D(256, 5, activation='relu')(x) x = layers.Conv1D(256, 5, activation='relu')(x) x = layers.GlobalMaxPooling1D()(x) x = layers.Dense(128, activation='relu')(x) age_prediction = layers.Dense(1, name='age')(x) income_prediction = layers.Dense(num_income_groups, activation='softmax', name='income')(x) gender_prediction = layers.Dense(1, activation='sigmoid', name='gender')(x) model = Model(posts_input, [age_prediction, income_prediction, gender_prediction])
②多输出模型的编译选项：多losses
model.compile(optimizer='rmsprop', loss=['mse', 'categorical_crossentropy', 'binary_crossentropy']) model.compile(optimizer='rmsprop', loss={'age': 'mse', 'income': 'categorical_crossentropy', 'gender': 'binary_crossentropy'})③多输出模型的编译选项：损失权重
model.compile(optimizer='rmsprop', loss=['mse', 'categorical_crossentropy', 'binary_crossentropy'], loss_weights=[0.25, 1., 10.]) model.compile(optimizer='rmsprop', loss={'age': 'mse', 'income': 'categorical_crossentropy', 'gender': 'binary_crossentropy'}, loss_weights={'age': 0.25, 'income': 1., 'gender': 10.})④喂数据
model.fit(posts, [age_targets, income_targets, gender_targets], epochs=10, batch_size=64) model.fit(posts, {'age': age_targets, 'income': income_targets, 'gender': gender_targets}, epochs=10, batch_size=64)五、
①callbacks
②TensorBoard
六、
①batch normalizetoin
②Hyperparameter optimization
③Model ensembling
preds_a = model_a.predict(x_val) preds_b = model_b.predict(x_val) preds_c = model_c.predict(x_val) preds_d = model_d.predict(x_val) final_preds = 0.5 * preds_a + 0.25 * preds_b + 0.1 * preds_c + 0.15 * preds_d
