问题描述
我正在使用 Kera 的函数式 API 创建人工神经网络 (ANN)。链接到数据 csv 文件：https://github.com/dpintof/SPX_Options_ANN/blob/master/MLP3/call_df.csv。重现问题的相关代码部分：
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow import keras
import tensorflow as tf
from tensorflow.keras import layers
# Data
call_df = pd.read_csv("call_df.csv")
call_X_train,call_X_test,call_y_train,call_y_test = train_test_split(call_df.drop(["Option_Average_Price"],axis = 1),call_df.Option_Average_Price,test_size = 0.01)
# Hyperparameters
n_hidden_layers = 2 # Number of hidden layers.
n_units = 128 # Number of neurons of the hidden layers.
# Create input layer
inputs = keras.Input(shape = (call_X_train.shape[1],))
x = layers.LeakyReLU(alpha = 1)(inputs)
"""
Function that creates a hidden layer by taking a tensor as input and applying a
modified ELU (MELU) activation function.
"""
def hl(tensor):
# Create custom MELU activation function
def melu(z):
return tf.cond(z > 0,lambda: ((z**2)/2 + 0.02*z) / (z - 2 + 1/0.49),lambda: 0.49*(keras.activations.exponential(z)-1))
y = layers.Dense(n_units,activation = melu)(tensor)
return y
# Create hidden layers
for _ in range(n_hidden_layers):
x = hl(x)
# Create output layer
outputs = layers.Dense(1,activation = keras.activations.softplus)(x)
# Actually create the model
model = keras.Model(inputs=inputs,outputs=outputs)
# QUICK TEST
model.compile(loss = "mse",optimizer = keras.optimizers.Adam())
history = model.fit(call_X_train,batch_size = 4096,epochs = 1,validation_split = 0.01,verbose = 1)
这是我在执行 model.fit(...) 时得到的错误（注意 4096 是我的批量大小，128 是隐藏层的神经元数量）：
InvalidArgumentError: The second input must be a scalar,but it has shape [4096,128]
[[{{node dense/cond/dense/BiasAdd/_5}}]] [Op:__inference_keras_scratch_graph_1074]
Function call stack:
keras_scratch_graph
我知道问题与自定义激活函数有关，因为如果我改用以下 hl 函数，程序运行良好：
def hl(tensor):
lr = layers.Dense(n_units,activation = layers.LeakyReLU())(tensor)
return lr
我在尝试像这样定义 melu(z) 时遇到了同样的错误：
@tf.function
def melu(z):
if z > 0:
return ((z**2)/2 + 0.02*z) / (z - 2 + 1/0.49)
else:
return 0.49*(keras.activations.exponential(z)-1)
从 How do you create a custom activation function with Keras? 开始，我也尝试了以下方法，但没有成功：
def hl(tensor):
# Create custom MELU activation function
def melu(z):
return tf.cond(z > 0,lambda: 0.49*(keras.activations.exponential(z)-1))
from keras.utils.generic_utils import get_custom_objects
get_custom_objects().update({'melu': layers.Activation(melu)})
x = layers.Dense(n_units)(tensor)
y = layers.Activation(melu)(x)
return y