python - 网格搜索适合不接受张量列表
问题描述
我有一个连体网络,我想使用 GridSearchCV 对其执行网格搜索。
所以我使用以下函数创建了一个模型:
def createMod(learn_rate=0.01, optimizer='Adam'):
#K.clear_session()
# network definition
base_network = create_base_network(input_shape)
input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)
# because we re-use the same instance `base_network`,
# the weights of the network will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)
distance = Lambda(euclidean_distance,
output_shape=eucl_dist_output_shape)([processed_a, processed_b])
prediction = Dense(1,activation='sigmoid')(distance)
model = Model([input_a, input_b], prediction)
if(optimizer=='SGD'):
opt = SGD(lr=learn_rate)
elif (optimizer=='RMSprop'):
opt = RMSprop(lr=learn_rate)
else:
opt = Adam(lr=learn_rate)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[accuracy])
return model
然后我创建 KerasClassifier 和 GridSearch,如下所示:
model = KerasClassifier(build_fn=createMod, verbose=0)
param_grid = dict(epochs=epochs, batch_size=batch_size, learn_rate=learn_rate,optimizer=optimizer)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)
X = [tr_pairs[:, 0], tr_pairs[:, 1]]
grid_result = grid.fit(X, tr_y)
但是,这会引发以下值错误:
ValueError:发现样本数量不一致的输入变量:[2, 1054]
tr_pairs[:, 0] 和 tr_pairs[:, 1] 的形状是 (1054, 6) 和 tr_y 是 (1054,)
基础网络的代码是:
def create_base_network(input_shape):
K.clear_session()
encoder = build_encoder(latent_dim, n_in)
decoder = build_decoder(latent_dim, n_in)
item = Input(shape=(n_in, ))
encoded_repr = encoder(item)
reconstructed_item = decoder(encoded_repr)
autoencoder = Model(item, reconstructed_item)
return autoencoder
编码器和解码器的代码是:
def build_encoder(latent_dim, input_dim):
input_layer = Input(shape=(input_dim, ))
h = Dense(32, activation='relu', activity_regularizer=regularizers.l1(10e-5))(input_layer)
h = Dropout(0.1)(h)
h = Dense(64, activation='relu')(h)
h = Dropout(0.1)(h)
# h = Dense(128, activation='relu')(h)
# h = Dropout(0.1)(h)
latent_repr = Dense(latent_dim, activation='relu')(h)
return Model(input_layer, latent_repr)
def build_decoder(latent_dim, input_dim):
model = Sequential()
# model.add(Dense(128, input_dim=latent_dim, activation='relu'))
# model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(input_dim, activation='relu'))
z = Input(shape=(latent_dim,))
item = model(z)
return Model(z, item)
当我执行正常的 keras 模型的 .fit 函数时,代码可以工作,但在这里不起作用。我的代码中是否有问题,或者只是无法在网格搜索中提供多个输入,如果是这样的话一种我仍然可以执行网格搜索的方法?
解决方案
这是传递多个输入的解决方法。我创建了一个虚拟模型,它接收格式为 (n_sample, 2, 6) 的 SINGLE 输入,然后使用 Lambda 层将其分成两部分。你可以根据你的连体结构修改它。
def createMod(optimizer='Adam'):
combi_input = Input((2,6)) # (n_sample, 2, 6)
input_a = Lambda(lambda x: x[:,0])(combi_input) # (n_sample, 6)
input_b = Lambda(lambda x: x[:,1])(combi_input) # (n_sample, 6)
c = Concatenate()([input_a,input_b])
x = Dense(32)(c)
prediction = Dense(1,activation='sigmoid')(x)
model = Model(combi_input, prediction)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics='accuracy')
return model
tr_pairs = np.random.uniform(0,1, (1054, 2, 6))
tr_y = np.random.randint(0,2, 1054)
model = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn=createMod, verbose=0)
batch_size = [10, 20]
epochs = [10, 5]
optimizer = ['adam','SGD']
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(tr_pairs, tr_y)
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