It's a good thing that training one model doesn't use all 100% of your CPU! Now we have space to train multiple models in parallel and speed up your overall training times.
NB: If you want to just speed up this model, look into GPUs or changing the hyperparameters like batch size and number of neurons (layer size).
Here's how you can use multiprocessing to train multiple models at the same time (using processes running in parallel on each separate CPU core of your machine).
The multiprocessing.Pool basically creates a pool of jobs that need doing. The processes will pick up these jobs and run them. When a job is finished, the process will pick up another job from the pool.
import time
import signal
import multiprocessing
def init_worker():
''' Add KeyboardInterrupt exception to mutliprocessing workers '''
signal.signal(signal.SIGINT, signal.SIG_IGN)
def train_model(layer_size):
'''
This code is parallelised and runs on each process
It trains a model with different layer sizes (hyperparameters)
It saves the model and returns the score (error)
'''
import keras
from keras.models import Sequential
from keras.layers import Dense
print(f'Training a model with layer size {layer_size}')
# build your model here
model_RNN = Sequential()
model_RNN.add(Dense(layer_size))
# fit the model (the bit that takes time!)
model_RNN.fit(...)
# lets demonstrate with a sleep timer
time.sleep(5)
# save trained model to a file
model_RNN.save(...)
# you can also return values eg. the eval score
return model_RNN.evaluate(...)
num_workers = 4
hyperparams = [800, 960, 1100]
pool = multiprocessing.Pool(num_workers, init_worker)
scores = pool.map(train_model, hyperparams)
print(scores)
Output:
Training a model with layer size 800
Training a model with layer size 960
Training a model with layer size 1100
[{'size':960,'score':1.0}, {'size':800,'score':1.2}, {'size':1100,'score':0.7}]
This is easily demonstrated with a time.sleep in the code. You'll see that all 3 processes start the training job, and then they all finish at about the same time. If this was single processed, you'd have to wait for each to finish before starting the next (yawn!).
EDIT
OP also wanted full code. This is difficult on Stack Overflow because I can't test in your environment and with your code. I've taken the liberty of copying and pasting your code into my template above. You may need to add some imports but this is as close as you'll get to "runnable" and "full" code.
import time
import signal
import numpy as np
import pandas as pd
import multiprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import accuracy_score
def init_worker():
''' Add KeyboardInterrupt exception to mutliprocessing workers '''
signal.signal(signal.SIGINT, signal.SIG_IGN)
def train_model(model_type):
'''
This code is parallelised and runs on each process
It trains a model with different layer sizes (hyperparameters)
It saves the model and returns the score (error)
'''
from keras.layers import LSTM, SimpleRNN, Dense, Activation
from keras.models import Sequential
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from keras.layers.normalization import BatchNormalization
print(f'Training a model: {model_type}')
callbacks = [
EarlyStopping(patience=10, verbose=1),
ReduceLROnPlateau(factor=0.1, patience=3, min_lr=0.00001, verbose=1),
]
model = Sequential()
if model_type == 'rnn':
model.add(SimpleRNN(units=1440, input_shape=(trainX.shape[1], trainX.shape[2])))
elif model_type == 'lstm':
model.add(LSTM(units=1440, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(480))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(
trainX,
trainY,
epochs=50,
batch_size=20,
validation_data=(testX, testY),
verbose=1,
callbacks=callbacks,
)
# predict
Y_Train_pred = model.predict(trainX)
Y_Test_pred = model.predict(testX)
train_MSE = mean_squared_error(trainY, Y_Train_pred)
test_MSE = mean_squared_error(testY, Y_Test_pred)
# you can also return values eg. the eval score
return {'type': model_type, 'train_MSE': train_MSE, 'test_MSE': test_MSE}
# Your code
# ---------
df = pd.read_csv("D:Train.csv", header=None)
index = [i for i in list(range(1440)) if i % 3 == 2]
Y_train = df[index]
df = df.values
# making history by using look-back to prediction next
def create_dataset(dataset, data_train, look_back=1):
dataX, dataY = [], []
print("Len:", len(dataset) - look_back - 1)
for i in range(len(dataset) - look_back - 1):
a = dataset[i : (i + look_back), :]
dataX.append(a)
dataY.append(data_train[i + look_back, :])
return np.array(dataX), np.array(dataY)
Y_train = np.array(Y_train)
df = np.array(df)
look_back = 10
trainX, trainY = create_dataset(df, Y_train, look_back=look_back)
# Split data into train & test
trainX, testX, trainY, testY = train_test_split(
trainX, trainY, test_size=0.2, shuffle=False
)
# My Code
# -------
num_workers = 2
model_types = ['rnn', 'lstm']
pool = multiprocessing.Pool(num_workers, init_worker)
scores = pool.map(train_model, model_types)
print(scores)
Output of the program:
[{'type': 'rnn', 'train_MSE': 0.06648435491248038, 'test_MSE': 0.062323388902691866},
{'type': 'lstm', 'train_MSE': 0.10114341514420684, 'test_MSE': 0.09998065769499974}]
