neural.py 文件源码

def create_model(L, hidden_sizes=[4], hidden_act='tanh', act='sigmoid', loss='binary_crossentropy',
                 Z=True, X=False, learning_rate=0.002,
                 normcentererr_p=None, batchnorm=0):
    in_dim = L**2 * (X+Z)
    out_dim = 2*L**2 * (X+Z)
    model = Sequential()
    model.add(Dense(int(hidden_sizes[0]*out_dim), input_dim=in_dim, kernel_initializer='glorot_uniform'))
    if batchnorm:
        model.add(BatchNormalization(momentum=batchnorm))
    model.add(Activation(hidden_act))
    for s in hidden_sizes[1:]:
        model.add(Dense(int(s*out_dim), kernel_initializer='glorot_uniform'))
        if batchnorm:
            model.add(BatchNormalization(momentum=batchnorm))
        model.add(Activation(hidden_act))
    model.add(Dense(out_dim, kernel_initializer='glorot_uniform'))
    if batchnorm:
        model.add(BatchNormalization(momentum=batchnorm))
    model.add(Activation(act))
    c = CodeCosts(L, ToricCode, Z, X, normcentererr_p)
    losses = {'e_binary_crossentropy':c.e_binary_crossentropy,
              's_binary_crossentropy':c.s_binary_crossentropy,
              'se_binary_crossentropy':c.se_binary_crossentropy}
    model.compile(loss=losses.get(loss,loss),
                  optimizer=Nadam(lr=learning_rate),
                  metrics=[c.triv_no_error, c.e_binary_crossentropy, c.s_binary_crossentropy]
                 )
    return model