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Я использую следующую простую архитектуру для обучения своей модели, но моя модель показывает 2-3 часа как прошедшее время за эпоху, когда я также использую маскированный ввод с моим дополненным вводом, почему это происходит именно так.

пожалуйста, найдите приведенный ниже код для моей модели

 class lstm_raw(tf.keras.Model):
  def __init__(self,name='spectrogram'):
    super().__init__(name=name)
    self.lstm = tf.keras.layers.LSTM(32,activation="tanh",kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2())
    self.dense1 = tf.keras.layers.Dense(64,activation="relu",kernel_initializer=tf.keras.initializers.he_uniform(seed=45))
    self.dense2 = tf.keras.layers.Dense(10,kernel_initializer=tf.keras.initializers.he_uniform(seed=45))
  def call(self,X):
    lstm_output = self.lstm(X[0],mask=X[1])
    dense1 = self.dense1(lstm_output)
    dense2 = self.dense2(dense1)
    return dense2

with tf.device('/device:GPU:0'):
  model1.fit(x=[X_train_pad_seq_test,X_train_mask_test],y=y_train,epochs=20,batch_size=4,steps_per_epoch=len(X_train_pad_seq_test)//4)
  

моя форма ввода, как показано ниже

 ((1400, 17640, 1), (1400, 17640, 1))
  

Виновник в вашем коде находится activation="relu" в вашем уровне LSTM.

Tensorflow ускоряет ячейки LSTM, используя cuDNN тогда и только тогда, когда активация установлена на tanh .

Замените relu на tanh , и вы увидите, как ваша модель взлетит!

Вот общий пример, который не должен занимать более 1-2 минут, макс.

 from pandas_datareader import data as wb
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
from sklearn.preprocessing import MinMaxScaler

start = '2019-06-30'
end = '2020-06-30'

tickers = ['GOOG']

thelen = len(tickers)

price_data = []
for ticker in tickers:
    prices = wb.DataReader(ticker, start = start, end = end, data_source='yahoo')[['Open','Adj Close']]
    price_data.append(prices.assign(ticker=ticker)[['ticker', 'Open', 'Adj Close']])

#names = np.reshape(price_data, (len(price_data), 1))

df = pd.concat(price_data)
df.reset_index(inplace=True)

for col in df.columns: 
    print(col) 
    
#used for setting the output figure size
rcParams['figure.figsize'] = 20,10
#to normalize the given input data
scaler = MinMaxScaler(feature_range=(0, 1))
#to read input data set (place the file name inside  ' ') as shown below


df['Adj Close'].plot()
plt.legend(loc=2)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

ntrain = 80
df_train = df.head(int(len(df)*(ntrain/100)))
ntest = -80
df_test = df.tail(int(len(df)*(ntest/100)))


#importing the packages 
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM

#dataframe creation
seriesdata = df.sort_index(ascending=True, axis=0)
new_seriesdata = pd.DataFrame(index=range(0,len(df)),columns=['Date','Adj Close'])
length_of_data=len(seriesdata)
for i in range(0,length_of_data):
    new_seriesdata['Date'][i] = seriesdata['Date'][i]
    new_seriesdata['Adj Close'][i] = seriesdata['Adj Close'][i]
#setting the index again
new_seriesdata.index = new_seriesdata.Date
new_seriesdata.drop('Date', axis=1, inplace=True)
#creating train and test sets this comprises the entire data’s present in the dataset
myseriesdataset = new_seriesdata.values
totrain = myseriesdataset[0:255,:]
tovalid = myseriesdataset[255:,:]
#converting dataset into x_train and y_train
scalerdata = MinMaxScaler(feature_range=(0, 1))
scale_data = scalerdata.fit_transform(myseriesdataset)
x_totrain, y_totrain = [], []
length_of_totrain=len(totrain)
for i in range(60,length_of_totrain):
    x_totrain.append(scale_data[i-60:i,0])
    y_totrain.append(scale_data[i,0])
x_totrain, y_totrain = np.array(x_totrain), np.array(y_totrain)
x_totrain = np.reshape(x_totrain, (x_totrain.shape[0],x_totrain.shape[1],1))


#LSTM neural network
lstm_model = Sequential()
lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(x_totrain.shape[1],1)))
lstm_model.add(LSTM(units=50))
lstm_model.add(Dense(1))
lstm_model.compile(loss='mean_squared_error', optimizer='adadelta')
lstm_model.fit(x_totrain, y_totrain, epochs=10, batch_size=1, verbose=2)
#predicting next data stock price
myinputs = new_seriesdata[len(new_seriesdata) - (len(tovalid) 1) - 60:].values
myinputs = myinputs.reshape(-1,1)
myinputs  = scalerdata.transform(myinputs)
tostore_test_result = []
for i in range(60,myinputs.shape[0]):
    tostore_test_result.append(myinputs[i-60:i,0])
tostore_test_result = np.array(tostore_test_result)
tostore_test_result = np.reshape(tostore_test_result,(tostore_test_result.shape[0],tostore_test_result.shape[1],1))
myclosing_priceresult = lstm_model.predict(tostore_test_result)
myclosing_priceresult = scalerdata.inverse_transform(myclosing_priceresult)
    
totrain = df_train
tovalid = df_test

#predicting next data stock price
myinputs = new_seriesdata[len(new_seriesdata) - (len(tovalid) 1) - 60:].values


#  Printing the next day’s predicted stock price. 
print(len(tostore_test_result));
print(myclosing_priceresult);
  

Для справки:

https://github.com/ASH-WICUS/Notebooks/blob/master/Long Short Term Memory — Stock Price Prediction.ipynb