使用theano的X_Train格式

Question

我想在已经使用Sklearn后，尝试Keras（Theano后端）进行回归。

为此，我使用了这个不错的教程 http://machinelearningmastery.com/regression-tutorial-keras-deep-learning-inbirary-python/并试图用我自己的培训数据替换培训数据。

import numpy
import pandas
import pickle
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler


[X, Y] = pickle.load(open("training_data_1_week_imp_lt_15.pkl", "rb"))


X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.5, random_state=42)
scaler = StandardScaler()
scaler.fit(X_train)  # Don't cheat - fit only on training data
X_train = scaler.transform(X_train)

X_test = scaler.transform(X_test)

print (X_train.shape)


# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(8, input_dim=8, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model


# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# evaluate model with standardized dataset
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=5, verbose=0)    

estimator.fit(numpy.array(X_train),y_train)

但是，我收到以下错误：

Exception: Error when checking model target: the list of Numpy arrays that you are 
passing to your model is not the size the model expected. Expected to see 1 
arrays but instead got the following list of 6252 arrays: ...

x的格式是通常的sklearn格式：print（x_train.shape）=（6252，8）

如何正确格式化X格式。

我尝试的是转置，但这无效。

我也已经搜索了网络，但找不到解决方案/解释。

谢谢！

编辑： 这是一个小样本文件 https://ufile.io/8a428

[X, Y] = pickle.load(open("test.pkl", "rb"))

Answer 1

我解决了这个问题（仍然把头撞在墙上）：

estimator.fit(numpy.array(X_train),numpy.array(y_train))

这有效。我不确定为什么。错误消息非常误导恕我直言。