小编Bis*_*dal的帖子

我正在尝试对我的超参数进行网格搜索，以调整深度学习架构。我有多个模型输入选项，我正在尝试使用 sklearn 的网格搜索 api。问题是，网格搜索api只接受单个数组作为输入，代码在检查数据大小维度时失败。（我的输入维度是5*数据点数，而根据sklearn api，它应该是数据点数*特征维度）。我的代码看起来像这样：

from keras.layers import Concatenate, Reshape, Input, Embedding, Dense, Dropout
from keras.models import Model
from keras.wrappers.scikit_learn import KerasClassifier

def model(hyparameters):
    a = Input(shape=(1,))
    b = Input(shape=(1,))
    c = Input(shape=(1,))
    d = Input(shape=(1,))
    e = Input(shape=(1,))

    //Some operations and I get a single output -->out
    model = Model([a, b, c, d, e], out)
    model.compile(optimizer='rmsprop',
                               loss='categorical_crossentropy',
                               metrics=['accuracy'])
    return model

k_model = KerasClassifier(build_fn=model, epochs=150, batch_size=512, verbose=2)
# define the grid search parameters
param_grid = hyperparameter options dict
grid = …

我正在尝试使用 keras.models.model_from_json 加载在 Keras 中训练的 RNN 模型架构，但出现上述错误

with open('model_architecture.json', 'r') as f:
    model = model_from_json(f.read(), custom_objects={'AttLayer':AttLayer})

# Load weights into the new model
model.load_weights('model_weights.h5')

这是我正在使用的自定义图层

class AttLayer(Layer):
    def __init__(self, attention_dim):
        self.init = initializers.get('normal')
        self.supports_masking = True
        self.attention_dim = attention_dim
        super(AttLayer, self).__init__()

    def build(self, input_shape):
        assert len(input_shape) == 3
        self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
        self.b = K.variable(self.init((self.attention_dim, )))
        self.u = K.variable(self.init((self.attention_dim, 1)))
        self.trainable_weights = [self.W, self.b, self.u]
        super(AttLayer, self).build(input_shape)

    def compute_mask(self, inputs, mask=None):
        return None

    def call(self, x, mask=None):
        # size of …