按照我上一篇文章中的建议，我重写了用于使用lib KERAS进行时间序列分析的脚本，但是在模型中获得了以下输出。

在递归网络中，输入形状应类似（批大小，时间步长，输入特征）。

输出

Traceback (most recent call last):
  File "rnrs.py",line 114,in <module>
    model = train_model(get_model(),X_train,Y_train,(X_dev,Y_dev),[plot_losses])
  File "rnrs.py",line 111,in train_model
    model.fit(X_train,epochs=200,batch_size=1024,validation_data=validation,callbacks=callbacks,shuffle=False)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py",line 1213,in fit
    self._make_train_function()
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py",line 316,in _make_train_function
    loss=self.total_loss)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\legacy\interfaces.py",line 91,in wrapper
    return func(*args,**kwargs)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\backend\tensorflow_backend.py",line 75,in symbolic_fn_wrapper
    return func(*args,**kwargs)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\optimizers.py",line 543,in get_updates
    p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\ops\math_ops.py",line 903,in binary_op_wrapper
    y,dtype_hint=x.dtype.base_dtype,name="y")
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\ops.py",line 1242,in convert_to_tensor_v2
    as_ref=False)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\ops.py",line 1296,in internal_convert_to_tensor
    ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\constant_op.py",line 286,in _constant_tensor_conversion_function
    return constant(v,name=name)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\constant_op.py",line 227,in constant
    allow_broadcast=True)
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\constant_op.py",line 265,in _constant_impl
    allow_broadcast=allow_broadcast))
  File "C:\Users\luis\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow_core\python\framework\tensor_util.py",line 437,in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

脚本

import pandas as pd

def load_dataset():
    ds = pd.read_csv('hour.csv')
    ds['dteday'] = pd.to_datetime(ds['dteday'])
    return ds

def one_hot_encoding(df,field):
    one_hot_encoded = pd.get_dummies(df[field])
    return  pd.concat([df.drop(field,axis=1),one_hot_encoded],axis=1)

def preprocess_dataset(df):

    df_reduced = df[['dteday','cnt','season','yr','mnth','hr','holiday','weekday','workingday','weathersit','temp','atemp','hum','windspeed']]
    df_reduced = one_hot_encoding(df_reduced,'season')
    df_reduced = one_hot_encoding(df_reduced,'mnth')
    df_reduced = one_hot_encoding(df_reduced,'hr')
    df_reduced = one_hot_encoding(df_reduced,'weekday')
    df_reduced = one_hot_encoding(df_reduced,'weathersit')

    return df_reduced

dataset = load_dataset()
dataset = preprocess_dataset(dataset)

from datetime import datetime

def filter_by_date(ds,start_date,end_date):

    start_date_parsed = datetime.strptime(start_date,"%Y-%m-%d") 
    start_end_parsed = datetime.strptime(end_date,"%Y-%m-%d")

    return ds[(ds['dteday'] >= start_date_parsed) & (ds['dteday'] <= start_end_parsed)]

train = filter_by_date(dataset,'2011-01-01','2012-10-31')
dev = filter_by_date(dataset,'2012-11-01','2012-11-30')
val = filter_by_date(dataset,'2012-12-01','2012-12-31')

import numpy as np

def reshape_dataset(ds):

    Y = ds['cnt'].values

    ds_values = ds.drop(['dteday','cnt'],axis=1).values
    X = np.reshape(ds_values,(ds_values.shape[0],1,ds_values.shape[1]))

    return X,Y

X_train,Y_train = reshape_dataset(train)
X_dev,Y_dev = reshape_dataset(dev)
X_val,Y_val = reshape_dataset(val)

import keras
from matplotlib import pyplot as plt
from IPython.display import clear_output

class PlotLosses(keras.callbacks.Callback):
    def on_train_begin(self,logs={}):
        self.i = 0
        self.x = []
        self.losses = []
        self.val_losses = []

        self.fig = plt.figure()        
        self.logs = []

    def on_epoch_end(self,epoch,logs={}):

        self.logs.append(logs)
        self.x.append(self.i)
        self.losses.append(logs.get('loss'))
        self.val_losses.append(logs.get('val_loss'))
        self.i += 1

        clear_output(wait=True)
        plt.plot(self.x,self.losses,label="loss")
        plt.plot(self.x,self.val_losses,label="val_loss")
        plt.legend()
        plt.show()

plot_losses = PlotLosses()

from keras.models import Model
from keras.layers import Input,Dense,LSTM,Dropout

def get_model():

    input = Input(shape=(1,58))
    x = LSTM(200)(input)
    x = Dropout(.5)(x)
    activation = Dense(1,activation='linear')(x)
    model = Model(inputs=input,outputs=activation)

    optimizer = keras.optimizers.Adam(lr=0.01,beta_1=0.9,beta_2=0.999,epsilon=None,decay=0.001,amsgrad=False)

    model.compile(loss='mean_absolute_error',optimizer=optimizer)
    model.summary()

    return model

get_model()

def train_model(model,validation,callbacks):

    model.fit(X_train,shuffle=False)
    return model

model = train_model(get_model(),[plot_losses])

数据集： Bike sharing dataset

所需出口