我正在训练几种模型，以便尝试找到适合该工作的最佳超参数。
我的数据是7天不同的像素值，回归结果是第7天的像素值。
我希望它理解起来不会太复杂，但是我正尝试从skratch训练每个模型，因此我将获得在超参数之间进行比较的真实结果（因此keras.backend.clear_session()）。
请参阅下面的代码：
import pickle
import pandas as pd
import numpy as np
import tensorflow
import os
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
def load_data(interval):
pickle_dir = 'C:/NDVI_DATA/pickle_pixel/'+str(interval)+'_d/'
picklx = open(pickle_dir+'X_pixle.pickle', 'rb')
pickly = open(pickle_dir+'y_pixle.pickle', 'rb')
X_inner = pickle.load(picklx)
y_inner = pickle.load(pickly)
return X_inner, y_inner
def optimizer_selector(name, learning_r):
if name == 'adam':
return_opti = keras.optimizers.Adam(learning_rate=learning_r)
elif name == 'SGD':
return_opti = keras.optimizers.SGD(learning_rate=learning_r, nesterov=True)
elif name == 'adamax':
return_opti = keras.optimizers.Adamax(learning_rate=learning_r)
elif name == 'adadelta':
return_opti = keras.optimizers.Adadelta(learning_rate=learning_r)
return return_opti
results = pd.DataFrame(columns=['Interval', 'Optimizer', 'LR', 'Epochs', 'Batch_size',
'Test_RMSE', 'History', 'Weights'])
def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
model_dir = 'C:/Users/user/PycharmProjects/NDVI/models/1'
os.chdir(model_dir)
for inter_days in [5, 10, 15, 20]:
os.chdir(model_dir)
print("\nStarting interval: "+str(inter_days)+"\n")
X, y = load_data(inter_days)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=20)
print("X_train shape: ", X_train.shape)
print("y_train shape: ", y_train.shape)
print("X_test shape: ", X_test.shape)
print("y_test shape: ", y_test.shape)
# Creating the model for the current interval
keras.backend.clear_session()
model = Sequential()
model.add(Dense(units=128, kernel_initializer='normal', input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(units=256, kernel_initializer='normal', activation='relu'))
model.add(Dense(units=256, kernel_initializer='normal', activation='relu'))
model.add(Dense(units=256, kernel_initializer='normal', activation='relu'))
model.add(Dense(units=1, kernel_initializer='normal', activation='linear'))
model.compile(loss='mean_absolute_error', optimizer='adam',
metrics=[keras.metrics.RootMeanSquaredError(name='rmse')])
Wsave = model.get_weights()
for opti in ['adam', 'SGD', 'adamax', 'adadelta']:
for lr in [0.01, 0.001, 0.0001]:
curr_opti = optimizer_selector(opti, lr)
model.compile(loss='mean_absolute_error', optimizer=curr_opti,
metrics=[keras.metrics.RootMeanSquaredError(name='rmse')])
for epochs_run in [1, 2, 3]:
for batch in [30, 50, 100, 200, 500]:
# Set up the directory which contains all the data
path = 'interval_' + str(inter_days)+'/'+opti+'/'+'lr_'+str(lr)+'/'+'epochs_'+str(epochs_run)+'/'+'batch_' + str(batch)
os.chdir(model_dir)
os.makedirs(path)
os.chdir(model_dir+'/'+path)
print('\nStarting fit for ' + str(inter_days) + ' days using ' + opti + ' optimizer, learning rate: '
+ str(lr) + ' epochs: ' + str(epochs_run) + ' and batch size: ' + str(batch))
# Reset weights before fit
model.set_weights(Wsave)
model.summary()
# Set callbacks
checkpoint_name = 'Weights--best.hdf5'
checkpoint = ModelCheckpoint(checkpoint_name, monitor='val_loss', verbose=1, save_best_only=True,
mode='auto')
callbacks_list = [checkpoint]
history = model.history #TODO: find how to use history
# Fit and results
model.fit(X_train, y_train, epochs=epochs_run, batch_size=batch, validation_split=0.2,
callbacks=[callbacks_list])
scores = model.evaluate(X_test, y_test)
weights = model.get_weights()
# Saving the model
model.save('saved_model')
# Add the model to the results DataFrame
model_summery = dict(Interval=inter_days, Optimizer=opti, LR=lr, Epochs=epochs_run,
Batch_size=batch, Test_RMSE=scores[1], History=history, Weights=weights),
results = results.append(pd.DataFrame(model_summery), ignore_index=True)
# Save all results to a pickle.
cwd = model_dir+'/'+'results_pickle'
os.chdir(cwd)
results_pickle = open("results.pickle", 'wb')
pickle.dump(results, results_pickle)
results_pickle.close()
我得到的输出是这样：
Starting fit for 5 days using SGD optimizer, learning rate: 0.01 epochs: 1 and batch size: 30
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 128) 1024
_________________________________________________________________
dense_1 (Dense) (None, 256) 33024
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
dense_3 (Dense) (None, 256) 65792
_________________________________________________________________
dense_4 (Dense) (None, 1) 257
=================================================================
Total params: 165,889
Trainable params: 165,889
Non-trainable params: 0
_________________________________________________________________
405/406 [============================>.] - ETA: 0s - loss: 0.1702 - rmse: 0.3044
Epoch 00001: val_loss improved from inf to 0.15796, saving model to Weights--best.hdf5
406/406 [==============================] - 5s 13ms/step - loss: 0.1702 - rmse: 0.3044 - val_loss: 0.1580 - val_rmse: 0.2883
204/204 [==============================] - 1s 5ms/step - loss: 0.1618 - rmse: 0.2921
Starting interval: 10
X_train shape: (12308, 13)
y_train shape: (12308, 1)
X_test shape: (5276, 13)
y_test shape: (5276, 1)
Starting fit for 10 days using adam optimizer, learning rate: 0.01 epochs: 1 and batch size: 30
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 128) 1792
_________________________________________________________________
dense_1 (Dense) (None, 256) 33024
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
dense_3 (Dense) (None, 256) 65792
_________________________________________________________________
dense_4 (Dense) (None, 1) 257
=================================================================
Total params: 166,657
Trainable params: 166,657
Non-trainable params: 0
_________________________________________________________________
326/329 [============================>.] - ETA: 0s - loss: nan - rmse: nan
Epoch 00001: val_loss did not improve from inf
329/329 [==============================] - 4s 13ms/step - loss: nan - rmse: nan - val_loss: nan - val_rmse: nan