一、加載.h5文件
from keras.models import load_model
model = load_model('***.h5')
model.summary
二、keras functional API 簡介
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、多輸入模型
①兩輸入的問答模型的functional API 的應用
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喂數據
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、多輸出模型
①三輸出模型的functional API 的應用
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多輸出模型的編譯選項：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多輸出模型的編譯選項：損失權重
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喂數據
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
七、
out = Concatenate()([model1.output, model2.output])
