问题描述
我正在尝试在GTM中创建一个用户定义的变量,该变量将允许我跟踪BING Ads中的电子商务转化价值,并且唯一需要使用的是第三方脚本,该脚本将以下示例数据输出到“确认”页面的源代码:
dataset_train = pd.read_csv(dataset_path)
training_set = dataset_train.iloc[:,:].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(x)
print(len(training_set_scaled))
print(len(dataset_train))
X_train = []
y_train = []
for i in range(past_days,len(training_set_scaled) - future_days + 1):
X_train.append(training_set_scaled[i - past_days:i,0])
y_train.append(training_set_scaled[i + future_days - 1:i + future_days,0])
X_train,y_train = np.array(X_train),np.array(y_train)
## Building and Training the RNN
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout
### Initialising the RNN
regressor = Sequential()
### Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,input_shape= (?),return_sequences=True))
regressor.add(Dropout(0.2))
### Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
### Adding a third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
### Adding a fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
### Adding the output layer
regressor.add(Dense(units=1))
### Compiling the RNN
regressor.compile(optimizer='adam',loss='mean_squared_error')
对于从GTM用户定义的变量列表中使用什么“变量类型”,我感到困惑,即使在这种情况下,即使有一个变量类型也可以在这里使用,我想提取变量值“ orderAmount”来自上面的输出,该输出出现在每个成功的订单上。
据我估计,为此设置一个单独的Bing Ads Universal Event Tracking标签非常简单。
解决方法
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