%matplotlib inline
import seaborn
import numpy as np
import matplotlib.pyplot as plt
X = np.random.random(1000) * 10 - 5
Y = np.sin(X)
training_num = 800
idx = np.arange(1000)
np.random.shuffle(idx)
training_idx = idx[:training_num]
test_idx = idx[training_num:]
trainingX, testX = X[training_idx], X[test_idx]
trainingY, testY = np.sin(trainingX), np.sin(testX)
from keras.models import Sequential
from keras.layers.core import Dense, Activation
model = Sequential()
model.add(Dense(30, input_shape=(1,)))
model.add(Activation("sigmoid"))
model.add(Dense(30))
model.add(Activation("sigmoid"))
model.add(Dense(1))
model.summary()
____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== dense_4 (Dense) (None, 30) 60 dense_input_2[0][0] ____________________________________________________________________________________________________ activation_3 (Activation) (None, 30) 0 dense_4[0][0] ____________________________________________________________________________________________________ dense_5 (Dense) (None, 30) 930 activation_3[0][0] ____________________________________________________________________________________________________ activation_4 (Activation) (None, 30) 0 dense_5[0][0] ____________________________________________________________________________________________________ dense_6 (Dense) (None, 1) 31 activation_4[0][0] ==================================================================================================== Total params: 1021 ____________________________________________________________________________________________________
model.compile(loss="mse", optimizer="sgd")
history = model.fit(trainingX, trainingY, batch_size=128, nb_epoch=100, validation_split=0.1, verbose=2)
Train on 720 samples, validate on 80 samples Epoch 1/100 0s - loss: 1.0418 - val_loss: 0.7496 Epoch 2/100 0s - loss: 0.5749 - val_loss: 0.6715 Epoch 3/100 0s - loss: 0.5439 - val_loss: 0.6627 Epoch 4/100 0s - loss: 0.5405 - val_loss: 0.6615 Epoch 5/100 0s - loss: 0.5395 - val_loss: 0.6593 Epoch 6/100 0s - loss: 0.5387 - val_loss: 0.6575 Epoch 7/100 0s - loss: 0.5387 - val_loss: 0.6570 Epoch 8/100 0s - loss: 0.5374 - val_loss: 0.6584 Epoch 9/100 0s - loss: 0.5363 - val_loss: 0.6572 Epoch 10/100 0s - loss: 0.5352 - val_loss: 0.6563 Epoch 11/100 0s - loss: 0.5346 - val_loss: 0.6561 Epoch 12/100 0s - loss: 0.5337 - val_loss: 0.6550 Epoch 13/100 0s - loss: 0.5336 - val_loss: 0.6540 Epoch 14/100 0s - loss: 0.5333 - val_loss: 0.6532 Epoch 15/100 0s - loss: 0.5320 - val_loss: 0.6530 Epoch 16/100 0s - loss: 0.5310 - val_loss: 0.6531 Epoch 17/100 0s - loss: 0.5305 - val_loss: 0.6533 Epoch 18/100 0s - loss: 0.5301 - val_loss: 0.6512 Epoch 19/100 0s - loss: 0.5297 - val_loss: 0.6515 Epoch 20/100 0s - loss: 0.5292 - val_loss: 0.6508 Epoch 21/100 0s - loss: 0.5286 - val_loss: 0.6516 Epoch 22/100 0s - loss: 0.5281 - val_loss: 0.6517 Epoch 23/100 0s - loss: 0.5294 - val_loss: 0.6501 Epoch 24/100 0s - loss: 0.5277 - val_loss: 0.6493 Epoch 25/100 0s - loss: 0.5277 - val_loss: 0.6505 Epoch 26/100 0s - loss: 0.5265 - val_loss: 0.6489 Epoch 27/100 0s - loss: 0.5258 - val_loss: 0.6500 Epoch 28/100 0s - loss: 0.5256 - val_loss: 0.6491 Epoch 29/100 0s - loss: 0.5249 - val_loss: 0.6497 Epoch 30/100 0s - loss: 0.5247 - val_loss: 0.6485 Epoch 31/100 0s - loss: 0.5246 - val_loss: 0.6474 Epoch 32/100 0s - loss: 0.5246 - val_loss: 0.6474 Epoch 33/100 0s - loss: 0.5240 - val_loss: 0.6476 Epoch 34/100 0s - loss: 0.5238 - val_loss: 0.6471 Epoch 35/100 0s - loss: 0.5232 - val_loss: 0.6468 Epoch 36/100 0s - loss: 0.5233 - val_loss: 0.6455 Epoch 37/100 0s - loss: 0.5224 - val_loss: 0.6458 Epoch 38/100 0s - loss: 0.5227 - val_loss: 0.6460 Epoch 39/100 0s - loss: 0.5232 - val_loss: 0.6444 Epoch 40/100 0s - loss: 0.5225 - val_loss: 0.6447 Epoch 41/100 0s - loss: 0.5219 - val_loss: 0.6463 Epoch 42/100 0s - loss: 0.5212 - val_loss: 0.6481 Epoch 43/100 0s - loss: 0.5213 - val_loss: 0.6470 Epoch 44/100 0s - loss: 0.5204 - val_loss: 0.6451 Epoch 45/100 0s - loss: 0.5201 - val_loss: 0.6450 Epoch 46/100 0s - loss: 0.5200 - val_loss: 0.6437 Epoch 47/100 0s - loss: 0.5201 - val_loss: 0.6441 Epoch 48/100 0s - loss: 0.5205 - val_loss: 0.6444 Epoch 49/100 0s - loss: 0.5199 - val_loss: 0.6437 Epoch 50/100 0s - loss: 0.5190 - val_loss: 0.6452 Epoch 51/100 0s - loss: 0.5191 - val_loss: 0.6444 Epoch 52/100 0s - loss: 0.5203 - val_loss: 0.6457 Epoch 53/100 0s - loss: 0.5189 - val_loss: 0.6425 Epoch 54/100 0s - loss: 0.5196 - val_loss: 0.6428 Epoch 55/100 0s - loss: 0.5189 - val_loss: 0.6437 Epoch 56/100 0s - loss: 0.5184 - val_loss: 0.6431 Epoch 57/100 0s - loss: 0.5198 - val_loss: 0.6425 Epoch 58/100 0s - loss: 0.5180 - val_loss: 0.6425 Epoch 59/100 0s - loss: 0.5178 - val_loss: 0.6429 Epoch 60/100 0s - loss: 0.5177 - val_loss: 0.6426 Epoch 61/100 0s - loss: 0.5174 - val_loss: 0.6427 Epoch 62/100 0s - loss: 0.5172 - val_loss: 0.6436 Epoch 63/100 0s - loss: 0.5171 - val_loss: 0.6432 Epoch 64/100 0s - loss: 0.5169 - val_loss: 0.6415 Epoch 65/100 0s - loss: 0.5176 - val_loss: 0.6416 Epoch 66/100 0s - loss: 0.5172 - val_loss: 0.6426 Epoch 67/100 0s - loss: 0.5165 - val_loss: 0.6426 Epoch 68/100 0s - loss: 0.5167 - val_loss: 0.6417 Epoch 69/100 0s - loss: 0.5165 - val_loss: 0.6419 Epoch 70/100 0s - loss: 0.5168 - val_loss: 0.6409 Epoch 71/100 0s - loss: 0.5167 - val_loss: 0.6410 Epoch 72/100 0s - loss: 0.5161 - val_loss: 0.6433 Epoch 73/100 0s - loss: 0.5162 - val_loss: 0.6414 Epoch 74/100 0s - loss: 0.5157 - val_loss: 0.6419 Epoch 75/100 0s - loss: 0.5161 - val_loss: 0.6423 Epoch 76/100 0s - loss: 0.5155 - val_loss: 0.6428 Epoch 77/100 0s - loss: 0.5158 - val_loss: 0.6442 Epoch 78/100 0s - loss: 0.5163 - val_loss: 0.6426 Epoch 79/100 0s - loss: 0.5156 - val_loss: 0.6429 Epoch 80/100 0s - loss: 0.5152 - val_loss: 0.6409 Epoch 81/100 0s - loss: 0.5153 - val_loss: 0.6421 Epoch 82/100 0s - loss: 0.5153 - val_loss: 0.6437 Epoch 83/100 0s - loss: 0.5156 - val_loss: 0.6414 Epoch 84/100 0s - loss: 0.5149 - val_loss: 0.6414 Epoch 85/100 0s - loss: 0.5151 - val_loss: 0.6427 Epoch 86/100 0s - loss: 0.5157 - val_loss: 0.6410 Epoch 87/100 0s - loss: 0.5154 - val_loss: 0.6404 Epoch 88/100 0s - loss: 0.5158 - val_loss: 0.6413 Epoch 89/100 0s - loss: 0.5144 - val_loss: 0.6414 Epoch 90/100 0s - loss: 0.5155 - val_loss: 0.6413 Epoch 91/100 0s - loss: 0.5164 - val_loss: 0.6416 Epoch 92/100 0s - loss: 0.5149 - val_loss: 0.6403 Epoch 93/100 0s - loss: 0.5149 - val_loss: 0.6399 Epoch 94/100 0s - loss: 0.5147 - val_loss: 0.6397 Epoch 95/100 0s - loss: 0.5143 - val_loss: 0.6427 Epoch 96/100 0s - loss: 0.5150 - val_loss: 0.6420 Epoch 97/100 0s - loss: 0.5141 - val_loss: 0.6414 Epoch 98/100 0s - loss: 0.5148 - val_loss: 0.6427 Epoch 99/100 0s - loss: 0.5139 - val_loss: 0.6424 Epoch 100/100 0s - loss: 0.5149 - val_loss: 0.6416
predictY = model.predict(testX)
plt.subplot(121)
plt.plot(np.arange(len(history.history["loss"])), history.history["loss"], color="r", alpha=0.3, label="loss")
plt.plot(np.arange(len(history.history["val_loss"])), history.history["val_loss"], color="b", alpha=0.3, label="val_loss")
plt.subplot(122)
plt.plot(testX, predictY, "bo", alpha=0.3)
plt.plot(testX, testY, "ro", alpha=0.3)
[<matplotlib.lines.Line2D at 0x10eb04910>]
history = model.fit(trainingX, trainingY, batch_size=128, nb_epoch=1000, validation_split=0.1, verbose=2)
Train on 720 samples, validate on 80 samples Epoch 1/1000 0s - loss: 0.5141 - val_loss: 0.6403 Epoch 2/1000 0s - loss: 0.5139 - val_loss: 0.6405 Epoch 3/1000 0s - loss: 0.5139 - val_loss: 0.6416 Epoch 4/1000 0s - loss: 0.5140 - val_loss: 0.6401 Epoch 5/1000 0s - loss: 0.5141 - val_loss: 0.6396 Epoch 6/1000 0s - loss: 0.5146 - val_loss: 0.6402 Epoch 7/1000 0s - loss: 0.5139 - val_loss: 0.6419 Epoch 8/1000 0s - loss: 0.5135 - val_loss: 0.6415 Epoch 9/1000 0s - loss: 0.5140 - val_loss: 0.6414 Epoch 10/1000 0s - loss: 0.5136 - val_loss: 0.6425 Epoch 11/1000 0s - loss: 0.5137 - val_loss: 0.6428 Epoch 12/1000 0s - loss: 0.5136 - val_loss: 0.6417 Epoch 13/1000 0s - loss: 0.5141 - val_loss: 0.6407 Epoch 14/1000 0s - loss: 0.5132 - val_loss: 0.6402 Epoch 15/1000 0s - loss: 0.5131 - val_loss: 0.6409 Epoch 16/1000 0s - loss: 0.5132 - val_loss: 0.6411 Epoch 17/1000 0s - loss: 0.5147 - val_loss: 0.6433 Epoch 18/1000 0s - loss: 0.5131 - val_loss: 0.6412 Epoch 19/1000 0s - loss: 0.5132 - val_loss: 0.6405 Epoch 20/1000 0s - loss: 0.5134 - val_loss: 0.6402 Epoch 21/1000 0s - loss: 0.5142 - val_loss: 0.6403 Epoch 22/1000 0s - loss: 0.5129 - val_loss: 0.6406 Epoch 23/1000 0s - loss: 0.5140 - val_loss: 0.6412 Epoch 24/1000 0s - loss: 0.5128 - val_loss: 0.6403 Epoch 25/1000 0s - loss: 0.5134 - val_loss: 0.6406 Epoch 26/1000 0s - loss: 0.5129 - val_loss: 0.6395 Epoch 27/1000 0s - loss: 0.5129 - val_loss: 0.6390 Epoch 28/1000 0s - loss: 0.5130 - val_loss: 0.6408 Epoch 29/1000 0s - loss: 0.5132 - val_loss: 0.6432 Epoch 30/1000 0s - loss: 0.5139 - val_loss: 0.6442 Epoch 31/1000 0s - loss: 0.5146 - val_loss: 0.6415 Epoch 32/1000 0s - loss: 0.5134 - val_loss: 0.6408 Epoch 33/1000 0s - loss: 0.5130 - val_loss: 0.6425 Epoch 34/1000 0s - loss: 0.5130 - val_loss: 0.6424 Epoch 35/1000 0s - loss: 0.5131 - val_loss: 0.6429 Epoch 36/1000 0s - loss: 0.5132 - val_loss: 0.6412 Epoch 37/1000 0s - loss: 0.5124 - val_loss: 0.6400 Epoch 38/1000 0s - loss: 0.5138 - val_loss: 0.6410 Epoch 39/1000 0s - loss: 0.5125 - val_loss: 0.6422 Epoch 40/1000 0s - loss: 0.5126 - val_loss: 0.6408 Epoch 41/1000 0s - loss: 0.5123 - val_loss: 0.6405 Epoch 42/1000 0s - loss: 0.5125 - val_loss: 0.6409 Epoch 43/1000 0s - loss: 0.5124 - val_loss: 0.6400 Epoch 44/1000 0s - loss: 0.5124 - val_loss: 0.6394 Epoch 45/1000 0s - loss: 0.5123 - val_loss: 0.6392 Epoch 46/1000 0s - loss: 0.5135 - val_loss: 0.6381 Epoch 47/1000 0s - loss: 0.5125 - val_loss: 0.6395 Epoch 48/1000 0s - loss: 0.5122 - val_loss: 0.6397 Epoch 49/1000 0s - loss: 0.5125 - val_loss: 0.6384 Epoch 50/1000 0s - loss: 0.5123 - val_loss: 0.6390 Epoch 51/1000 0s - loss: 0.5124 - val_loss: 0.6391 Epoch 52/1000 0s - loss: 0.5128 - val_loss: 0.6394 Epoch 53/1000 0s - loss: 0.5119 - val_loss: 0.6404 Epoch 54/1000 0s - loss: 0.5123 - val_loss: 0.6410 Epoch 55/1000 0s - loss: 0.5121 - val_loss: 0.6390 Epoch 56/1000 0s - loss: 0.5121 - val_loss: 0.6393 Epoch 57/1000 0s - loss: 0.5120 - val_loss: 0.6388 Epoch 58/1000 0s - loss: 0.5119 - val_loss: 0.6398 Epoch 59/1000 0s - loss: 0.5117 - val_loss: 0.6403 Epoch 60/1000 0s - loss: 0.5122 - val_loss: 0.6404 Epoch 61/1000 0s - loss: 0.5117 - val_loss: 0.6391 Epoch 62/1000 0s - loss: 0.5122 - val_loss: 0.6427 Epoch 63/1000 0s - loss: 0.5122 - val_loss: 0.6400 Epoch 64/1000 0s - loss: 0.5120 - val_loss: 0.6389 Epoch 65/1000 0s - loss: 0.5116 - val_loss: 0.6389 Epoch 66/1000 0s - loss: 0.5131 - val_loss: 0.6374 Epoch 67/1000 0s - loss: 0.5123 - val_loss: 0.6386 Epoch 68/1000 0s - loss: 0.5117 - val_loss: 0.6413 Epoch 69/1000 0s - loss: 0.5117 - val_loss: 0.6388 Epoch 70/1000 0s - loss: 0.5114 - val_loss: 0.6391 Epoch 71/1000 0s - loss: 0.5119 - val_loss: 0.6431 Epoch 72/1000 0s - loss: 0.5117 - val_loss: 0.6397 Epoch 73/1000 0s - loss: 0.5114 - val_loss: 0.6402 Epoch 74/1000 0s - loss: 0.5117 - val_loss: 0.6405 Epoch 75/1000 0s - loss: 0.5115 - val_loss: 0.6398 Epoch 76/1000 0s - loss: 0.5116 - val_loss: 0.6388 Epoch 77/1000 0s - loss: 0.5121 - val_loss: 0.6399 Epoch 78/1000 0s - loss: 0.5116 - val_loss: 0.6389 Epoch 79/1000 0s - loss: 0.5111 - val_loss: 0.6385 Epoch 80/1000 0s - loss: 0.5112 - val_loss: 0.6403 Epoch 81/1000 0s - loss: 0.5115 - val_loss: 0.6384 Epoch 82/1000 0s - loss: 0.5121 - val_loss: 0.6390 Epoch 83/1000 0s - loss: 0.5112 - val_loss: 0.6394 Epoch 84/1000 0s - loss: 0.5119 - val_loss: 0.6434 Epoch 85/1000 0s - loss: 0.5119 - val_loss: 0.6383 Epoch 86/1000 0s - loss: 0.5124 - val_loss: 0.6394 Epoch 87/1000 0s - loss: 0.5118 - val_loss: 0.6398 Epoch 88/1000 0s - loss: 0.5114 - val_loss: 0.6405 Epoch 89/1000 0s - loss: 0.5115 - val_loss: 0.6411 Epoch 90/1000 0s - loss: 0.5114 - val_loss: 0.6398 Epoch 91/1000 0s - loss: 0.5110 - val_loss: 0.6403 Epoch 92/1000 0s - loss: 0.5111 - val_loss: 0.6380 Epoch 93/1000 0s - loss: 0.5110 - val_loss: 0.6413 Epoch 94/1000 0s - loss: 0.5109 - val_loss: 0.6373 Epoch 95/1000 0s - loss: 0.5117 - val_loss: 0.6397 Epoch 96/1000 0s - loss: 0.5112 - val_loss: 0.6376 Epoch 97/1000 0s - loss: 0.5115 - val_loss: 0.6378 Epoch 98/1000 0s - loss: 0.5107 - val_loss: 0.6377 Epoch 99/1000 0s - loss: 0.5108 - val_loss: 0.6384 Epoch 100/1000 0s - loss: 0.5108 - val_loss: 0.6381 Epoch 101/1000 0s - loss: 0.5113 - val_loss: 0.6376 Epoch 102/1000 0s - loss: 0.5118 - val_loss: 0.6378 Epoch 103/1000 0s - loss: 0.5116 - val_loss: 0.6386 Epoch 104/1000 0s - loss: 0.5105 - val_loss: 0.6401 Epoch 105/1000 0s - loss: 0.5108 - val_loss: 0.6385 Epoch 106/1000 0s - loss: 0.5103 - val_loss: 0.6404 Epoch 107/1000 0s - loss: 0.5109 - val_loss: 0.6403 Epoch 108/1000 0s - loss: 0.5103 - val_loss: 0.6376 Epoch 109/1000 0s - loss: 0.5103 - val_loss: 0.6377 Epoch 110/1000 0s - loss: 0.5104 - val_loss: 0.6383 Epoch 111/1000 0s - loss: 0.5102 - val_loss: 0.6375 Epoch 112/1000 0s - loss: 0.5102 - val_loss: 0.6368 Epoch 113/1000 0s - loss: 0.5106 - val_loss: 0.6374 Epoch 114/1000 0s - loss: 0.5112 - val_loss: 0.6383 Epoch 115/1000 0s - loss: 0.5102 - val_loss: 0.6374 Epoch 116/1000 0s - loss: 0.5104 - val_loss: 0.6375 Epoch 117/1000 0s - loss: 0.5101 - val_loss: 0.6380 Epoch 118/1000 0s - loss: 0.5102 - val_loss: 0.6381 Epoch 119/1000 0s - loss: 0.5113 - val_loss: 0.6362 Epoch 120/1000 0s - loss: 0.5107 - val_loss: 0.6373 Epoch 121/1000 0s - loss: 0.5100 - val_loss: 0.6379 Epoch 122/1000 0s - loss: 0.5102 - val_loss: 0.6395 Epoch 123/1000 0s - loss: 0.5098 - val_loss: 0.6371 Epoch 124/1000 0s - loss: 0.5107 - val_loss: 0.6378 Epoch 125/1000 0s - loss: 0.5106 - val_loss: 0.6370 Epoch 126/1000 0s - loss: 0.5101 - val_loss: 0.6375 Epoch 127/1000 0s - loss: 0.5098 - val_loss: 0.6398 Epoch 128/1000 0s - loss: 0.5115 - val_loss: 0.6377 Epoch 129/1000 0s - loss: 0.5103 - val_loss: 0.6365 Epoch 130/1000 0s - loss: 0.5097 - val_loss: 0.6356 Epoch 131/1000 0s - loss: 0.5103 - val_loss: 0.6358 Epoch 132/1000 0s - loss: 0.5098 - val_loss: 0.6367 Epoch 133/1000 0s - loss: 0.5096 - val_loss: 0.6367 Epoch 134/1000 0s - loss: 0.5096 - val_loss: 0.6374 Epoch 135/1000 0s - loss: 0.5099 - val_loss: 0.6359 Epoch 136/1000 0s - loss: 0.5102 - val_loss: 0.6355 Epoch 137/1000 0s - loss: 0.5098 - val_loss: 0.6367 Epoch 138/1000 0s - loss: 0.5098 - val_loss: 0.6362 Epoch 139/1000 0s - loss: 0.5097 - val_loss: 0.6380 Epoch 140/1000 0s - loss: 0.5099 - val_loss: 0.6379 Epoch 141/1000 0s - loss: 0.5103 - val_loss: 0.6372 Epoch 142/1000 0s - loss: 0.5095 - val_loss: 0.6392 Epoch 143/1000 0s - loss: 0.5093 - val_loss: 0.6366 Epoch 144/1000 0s - loss: 0.5095 - val_loss: 0.6354 Epoch 145/1000 0s - loss: 0.5099 - val_loss: 0.6375 Epoch 146/1000 0s - loss: 0.5095 - val_loss: 0.6393 Epoch 147/1000 0s - loss: 0.5093 - val_loss: 0.6391 Epoch 148/1000 0s - loss: 0.5091 - val_loss: 0.6383 Epoch 149/1000 0s - loss: 0.5094 - val_loss: 0.6393 Epoch 150/1000 0s - loss: 0.5093 - val_loss: 0.6360 Epoch 151/1000 0s - loss: 0.5099 - val_loss: 0.6355 Epoch 152/1000 0s - loss: 0.5090 - val_loss: 0.6364 Epoch 153/1000 0s - loss: 0.5095 - val_loss: 0.6352 Epoch 154/1000 0s - loss: 0.5091 - val_loss: 0.6369 Epoch 155/1000 0s - loss: 0.5089 - val_loss: 0.6372 Epoch 156/1000 0s - loss: 0.5089 - val_loss: 0.6365 Epoch 157/1000 0s - loss: 0.5089 - val_loss: 0.6353 Epoch 158/1000 0s - loss: 0.5091 - val_loss: 0.6359 Epoch 159/1000 0s - loss: 0.5089 - val_loss: 0.6349 Epoch 160/1000 0s - loss: 0.5089 - val_loss: 0.6372 Epoch 161/1000 0s - loss: 0.5103 - val_loss: 0.6355 Epoch 162/1000 0s - loss: 0.5093 - val_loss: 0.6361 Epoch 163/1000 0s - loss: 0.5094 - val_loss: 0.6361 Epoch 164/1000 0s - loss: 0.5084 - val_loss: 0.6360 Epoch 165/1000 0s - loss: 0.5084 - val_loss: 0.6362 Epoch 166/1000 0s - loss: 0.5085 - val_loss: 0.6367 Epoch 167/1000 0s - loss: 0.5084 - val_loss: 0.6379 Epoch 168/1000 0s - loss: 0.5091 - val_loss: 0.6354 Epoch 169/1000 0s - loss: 0.5082 - val_loss: 0.6372 Epoch 170/1000 0s - loss: 0.5087 - val_loss: 0.6354 Epoch 171/1000 0s - loss: 0.5089 - val_loss: 0.6353 Epoch 172/1000 0s - loss: 0.5080 - val_loss: 0.6347 Epoch 173/1000 0s - loss: 0.5081 - val_loss: 0.6351 Epoch 174/1000 0s - loss: 0.5084 - val_loss: 0.6348 Epoch 175/1000 0s - loss: 0.5082 - val_loss: 0.6349 Epoch 176/1000 0s - loss: 0.5089 - val_loss: 0.6353 Epoch 177/1000 0s - loss: 0.5081 - val_loss: 0.6364 Epoch 178/1000 0s - loss: 0.5087 - val_loss: 0.6362 Epoch 179/1000 0s - loss: 0.5081 - val_loss: 0.6356 Epoch 180/1000 0s - loss: 0.5082 - val_loss: 0.6347 Epoch 181/1000 0s - loss: 0.5077 - val_loss: 0.6353 Epoch 182/1000 0s - loss: 0.5082 - val_loss: 0.6346 Epoch 183/1000 0s - loss: 0.5078 - val_loss: 0.6351 Epoch 184/1000 0s - loss: 0.5080 - val_loss: 0.6353 Epoch 185/1000 0s - loss: 0.5082 - val_loss: 0.6344 Epoch 186/1000 0s - loss: 0.5075 - val_loss: 0.6332 Epoch 187/1000 0s - loss: 0.5077 - val_loss: 0.6346 Epoch 188/1000 0s - loss: 0.5076 - val_loss: 0.6342 Epoch 189/1000 0s - loss: 0.5076 - val_loss: 0.6345 Epoch 190/1000 0s - loss: 0.5077 - val_loss: 0.6363 Epoch 191/1000 0s - loss: 0.5075 - val_loss: 0.6360 Epoch 192/1000 0s - loss: 0.5071 - val_loss: 0.6336 Epoch 193/1000 0s - loss: 0.5073 - val_loss: 0.6344 Epoch 194/1000 0s - loss: 0.5073 - val_loss: 0.6333 Epoch 195/1000 0s - loss: 0.5071 - val_loss: 0.6336 Epoch 196/1000 0s - loss: 0.5074 - val_loss: 0.6346 Epoch 197/1000 0s - loss: 0.5073 - val_loss: 0.6343 Epoch 198/1000 0s - loss: 0.5071 - val_loss: 0.6329 Epoch 199/1000 0s - loss: 0.5076 - val_loss: 0.6348 Epoch 200/1000 0s - loss: 0.5073 - val_loss: 0.6333 Epoch 201/1000 0s - loss: 0.5071 - val_loss: 0.6325 Epoch 202/1000 0s - loss: 0.5068 - val_loss: 0.6329 Epoch 203/1000 0s - loss: 0.5065 - val_loss: 0.6341 Epoch 204/1000 0s - loss: 0.5064 - val_loss: 0.6354 Epoch 205/1000 0s - loss: 0.5067 - val_loss: 0.6337 Epoch 206/1000 0s - loss: 0.5069 - val_loss: 0.6338 Epoch 207/1000 0s - loss: 0.5066 - val_loss: 0.6329 Epoch 208/1000 0s - loss: 0.5068 - val_loss: 0.6332 Epoch 209/1000 0s - loss: 0.5072 - val_loss: 0.6341 Epoch 210/1000 0s - loss: 0.5067 - val_loss: 0.6334 Epoch 211/1000 0s - loss: 0.5068 - val_loss: 0.6329 Epoch 212/1000 0s - loss: 0.5060 - val_loss: 0.6338 Epoch 213/1000 0s - loss: 0.5065 - val_loss: 0.6349 Epoch 214/1000 0s - loss: 0.5063 - val_loss: 0.6327 Epoch 215/1000 0s - loss: 0.5061 - val_loss: 0.6315 Epoch 216/1000 0s - loss: 0.5059 - val_loss: 0.6319 Epoch 217/1000 0s - 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val_loss: 0.2366 Epoch 783/1000 0s - loss: 0.1860 - val_loss: 0.2347 Epoch 784/1000 0s - loss: 0.1846 - val_loss: 0.2345 Epoch 785/1000 0s - loss: 0.1835 - val_loss: 0.2320 Epoch 786/1000 0s - loss: 0.1828 - val_loss: 0.2308 Epoch 787/1000 0s - loss: 0.1812 - val_loss: 0.2296 Epoch 788/1000 0s - loss: 0.1801 - val_loss: 0.2282 Epoch 789/1000 0s - loss: 0.1791 - val_loss: 0.2271 Epoch 790/1000 0s - loss: 0.1778 - val_loss: 0.2251 Epoch 791/1000 0s - loss: 0.1775 - val_loss: 0.2237 Epoch 792/1000 0s - loss: 0.1754 - val_loss: 0.2223 Epoch 793/1000 0s - loss: 0.1745 - val_loss: 0.2208 Epoch 794/1000 0s - loss: 0.1733 - val_loss: 0.2196 Epoch 795/1000 0s - loss: 0.1720 - val_loss: 0.2176 Epoch 796/1000 0s - loss: 0.1709 - val_loss: 0.2178 Epoch 797/1000 0s - loss: 0.1702 - val_loss: 0.2159 Epoch 798/1000 0s - loss: 0.1687 - val_loss: 0.2134 Epoch 799/1000 0s - loss: 0.1679 - val_loss: 0.2118 Epoch 800/1000 0s - loss: 0.1666 - val_loss: 0.2103 Epoch 801/1000 0s - loss: 0.1651 - val_loss: 0.2092 Epoch 802/1000 0s - loss: 0.1640 - val_loss: 0.2079 Epoch 803/1000 0s - loss: 0.1628 - val_loss: 0.2071 Epoch 804/1000 0s - loss: 0.1621 - val_loss: 0.2052 Epoch 805/1000 0s - loss: 0.1609 - val_loss: 0.2042 Epoch 806/1000 0s - loss: 0.1596 - val_loss: 0.2022 Epoch 807/1000 0s - loss: 0.1589 - val_loss: 0.2008 Epoch 808/1000 0s - loss: 0.1576 - val_loss: 0.1995 Epoch 809/1000 0s - loss: 0.1564 - val_loss: 0.1980 Epoch 810/1000 0s - loss: 0.1554 - val_loss: 0.1968 Epoch 811/1000 0s - loss: 0.1540 - val_loss: 0.1951 Epoch 812/1000 0s - loss: 0.1532 - val_loss: 0.1942 Epoch 813/1000 0s - loss: 0.1517 - val_loss: 0.1925 Epoch 814/1000 0s - loss: 0.1507 - val_loss: 0.1919 Epoch 815/1000 0s - loss: 0.1496 - val_loss: 0.1901 Epoch 816/1000 0s - loss: 0.1485 - val_loss: 0.1884 Epoch 817/1000 0s - loss: 0.1476 - val_loss: 0.1880 Epoch 818/1000 0s - loss: 0.1465 - val_loss: 0.1864 Epoch 819/1000 0s - loss: 0.1453 - val_loss: 0.1851 Epoch 820/1000 0s - loss: 0.1443 - val_loss: 0.1835 Epoch 821/1000 0s - loss: 0.1433 - val_loss: 0.1822 Epoch 822/1000 0s - loss: 0.1423 - val_loss: 0.1806 Epoch 823/1000 0s - loss: 0.1414 - val_loss: 0.1790 Epoch 824/1000 0s - loss: 0.1402 - val_loss: 0.1782 Epoch 825/1000 0s - loss: 0.1390 - val_loss: 0.1775 Epoch 826/1000 0s - loss: 0.1381 - val_loss: 0.1755 Epoch 827/1000 0s - loss: 0.1371 - val_loss: 0.1742 Epoch 828/1000 0s - loss: 0.1360 - val_loss: 0.1732 Epoch 829/1000 0s - loss: 0.1348 - val_loss: 0.1722 Epoch 830/1000 0s - loss: 0.1340 - val_loss: 0.1709 Epoch 831/1000 0s - loss: 0.1330 - val_loss: 0.1689 Epoch 832/1000 0s - loss: 0.1318 - val_loss: 0.1676 Epoch 833/1000 0s - loss: 0.1310 - val_loss: 0.1676 Epoch 834/1000 0s - loss: 0.1305 - val_loss: 0.1656 Epoch 835/1000 0s - loss: 0.1292 - val_loss: 0.1641 Epoch 836/1000 0s - loss: 0.1280 - val_loss: 0.1625 Epoch 837/1000 0s - loss: 0.1270 - val_loss: 0.1615 Epoch 838/1000 0s - loss: 0.1260 - val_loss: 0.1602 Epoch 839/1000 0s - loss: 0.1250 - val_loss: 0.1597 Epoch 840/1000 0s - loss: 0.1241 - val_loss: 0.1579 Epoch 841/1000 0s - loss: 0.1230 - val_loss: 0.1569 Epoch 842/1000 0s - loss: 0.1222 - val_loss: 0.1557 Epoch 843/1000 0s - loss: 0.1212 - val_loss: 0.1550 Epoch 844/1000 0s - loss: 0.1207 - val_loss: 0.1530 Epoch 845/1000 0s - loss: 0.1193 - val_loss: 0.1524 Epoch 846/1000 0s - loss: 0.1184 - val_loss: 0.1512 Epoch 847/1000 0s - loss: 0.1177 - val_loss: 0.1499 Epoch 848/1000 0s - loss: 0.1164 - val_loss: 0.1486 Epoch 849/1000 0s - loss: 0.1155 - val_loss: 0.1471 Epoch 850/1000 0s - loss: 0.1146 - val_loss: 0.1459 Epoch 851/1000 0s - loss: 0.1139 - val_loss: 0.1453 Epoch 852/1000 0s - loss: 0.1128 - val_loss: 0.1441 Epoch 853/1000 0s - loss: 0.1120 - val_loss: 0.1428 Epoch 854/1000 0s - loss: 0.1110 - val_loss: 0.1424 Epoch 855/1000 0s - loss: 0.1104 - val_loss: 0.1403 Epoch 856/1000 0s - loss: 0.1091 - val_loss: 0.1390 Epoch 857/1000 0s - loss: 0.1086 - val_loss: 0.1383 Epoch 858/1000 0s - loss: 0.1073 - val_loss: 0.1373 Epoch 859/1000 0s - loss: 0.1066 - val_loss: 0.1358 Epoch 860/1000 0s - loss: 0.1057 - val_loss: 0.1346 Epoch 861/1000 0s - loss: 0.1050 - val_loss: 0.1335 Epoch 862/1000 0s - loss: 0.1040 - val_loss: 0.1329 Epoch 863/1000 0s - loss: 0.1032 - val_loss: 0.1318 Epoch 864/1000 0s - loss: 0.1023 - val_loss: 0.1308 Epoch 865/1000 0s - loss: 0.1014 - val_loss: 0.1294 Epoch 866/1000 0s - loss: 0.1007 - val_loss: 0.1286 Epoch 867/1000 0s - loss: 0.0998 - val_loss: 0.1280 Epoch 868/1000 0s - loss: 0.0991 - val_loss: 0.1267 Epoch 869/1000 0s - loss: 0.0982 - val_loss: 0.1254 Epoch 870/1000 0s - loss: 0.0975 - val_loss: 0.1243 Epoch 871/1000 0s - loss: 0.0966 - val_loss: 0.1233 Epoch 872/1000 0s - loss: 0.0959 - val_loss: 0.1229 Epoch 873/1000 0s - loss: 0.0952 - val_loss: 0.1213 Epoch 874/1000 0s - loss: 0.0944 - val_loss: 0.1208 Epoch 875/1000 0s - loss: 0.0935 - val_loss: 0.1196 Epoch 876/1000 0s - loss: 0.0926 - val_loss: 0.1184 Epoch 877/1000 0s - loss: 0.0921 - val_loss: 0.1176 Epoch 878/1000 0s - loss: 0.0913 - val_loss: 0.1165 Epoch 879/1000 0s - loss: 0.0904 - val_loss: 0.1155 Epoch 880/1000 0s - loss: 0.0896 - val_loss: 0.1143 Epoch 881/1000 0s - loss: 0.0890 - val_loss: 0.1140 Epoch 882/1000 0s - loss: 0.0882 - val_loss: 0.1126 Epoch 883/1000 0s - loss: 0.0874 - val_loss: 0.1116 Epoch 884/1000 0s - loss: 0.0868 - val_loss: 0.1106 Epoch 885/1000 0s - loss: 0.0863 - val_loss: 0.1097 Epoch 886/1000 0s - loss: 0.0853 - val_loss: 0.1091 Epoch 887/1000 0s - loss: 0.0844 - val_loss: 0.1081 Epoch 888/1000 0s - loss: 0.0839 - val_loss: 0.1070 Epoch 889/1000 0s - loss: 0.0830 - val_loss: 0.1067 Epoch 890/1000 0s - loss: 0.0825 - val_loss: 0.1056 Epoch 891/1000 0s - loss: 0.0819 - val_loss: 0.1043 Epoch 892/1000 0s - loss: 0.0814 - val_loss: 0.1034 Epoch 893/1000 0s - loss: 0.0805 - val_loss: 0.1028 Epoch 894/1000 0s - loss: 0.0797 - val_loss: 0.1018 Epoch 895/1000 0s - loss: 0.0790 - val_loss: 0.1009 Epoch 896/1000 0s - loss: 0.0785 - val_loss: 0.1007 Epoch 897/1000 0s - loss: 0.0779 - val_loss: 0.0995 Epoch 898/1000 0s - loss: 0.0771 - val_loss: 0.0987 Epoch 899/1000 0s - loss: 0.0764 - val_loss: 0.0980 Epoch 900/1000 0s - loss: 0.0761 - val_loss: 0.0974 Epoch 901/1000 0s - loss: 0.0752 - val_loss: 0.0961 Epoch 902/1000 0s - loss: 0.0746 - val_loss: 0.0953 Epoch 903/1000 0s - loss: 0.0740 - val_loss: 0.0945 Epoch 904/1000 0s - loss: 0.0732 - val_loss: 0.0937 Epoch 905/1000 0s - loss: 0.0729 - val_loss: 0.0929 Epoch 906/1000 0s - loss: 0.0721 - val_loss: 0.0921 Epoch 907/1000 0s - loss: 0.0716 - val_loss: 0.0914 Epoch 908/1000 0s - loss: 0.0709 - val_loss: 0.0909 Epoch 909/1000 0s - loss: 0.0704 - val_loss: 0.0900 Epoch 910/1000 0s - loss: 0.0698 - val_loss: 0.0889 Epoch 911/1000 0s - loss: 0.0692 - val_loss: 0.0884 Epoch 912/1000 0s - loss: 0.0688 - val_loss: 0.0875 Epoch 913/1000 0s - loss: 0.0681 - val_loss: 0.0868 Epoch 914/1000 0s - loss: 0.0675 - val_loss: 0.0864 Epoch 915/1000 0s - loss: 0.0670 - val_loss: 0.0854 Epoch 916/1000 0s - loss: 0.0665 - val_loss: 0.0849 Epoch 917/1000 0s - loss: 0.0658 - val_loss: 0.0843 Epoch 918/1000 0s - loss: 0.0653 - val_loss: 0.0832 Epoch 919/1000 0s - loss: 0.0648 - val_loss: 0.0825 Epoch 920/1000 0s - loss: 0.0642 - val_loss: 0.0820 Epoch 921/1000 0s - loss: 0.0638 - val_loss: 0.0815 Epoch 922/1000 0s - loss: 0.0634 - val_loss: 0.0807 Epoch 923/1000 0s - loss: 0.0626 - val_loss: 0.0798 Epoch 924/1000 0s - loss: 0.0622 - val_loss: 0.0792 Epoch 925/1000 0s - loss: 0.0622 - val_loss: 0.0786 Epoch 926/1000 0s - loss: 0.0613 - val_loss: 0.0781 Epoch 927/1000 0s - loss: 0.0607 - val_loss: 0.0779 Epoch 928/1000 0s - loss: 0.0603 - val_loss: 0.0769 Epoch 929/1000 0s - loss: 0.0597 - val_loss: 0.0760 Epoch 930/1000 0s - loss: 0.0593 - val_loss: 0.0754 Epoch 931/1000 0s - loss: 0.0588 - val_loss: 0.0753 Epoch 932/1000 0s - loss: 0.0583 - val_loss: 0.0742 Epoch 933/1000 0s - loss: 0.0578 - val_loss: 0.0736 Epoch 934/1000 0s - loss: 0.0574 - val_loss: 0.0732 Epoch 935/1000 0s - loss: 0.0571 - val_loss: 0.0724 Epoch 936/1000 0s - loss: 0.0565 - val_loss: 0.0719 Epoch 937/1000 0s - loss: 0.0561 - val_loss: 0.0712 Epoch 938/1000 0s - loss: 0.0557 - val_loss: 0.0709 Epoch 939/1000 0s - loss: 0.0553 - val_loss: 0.0703 Epoch 940/1000 0s - loss: 0.0547 - val_loss: 0.0701 Epoch 941/1000 0s - loss: 0.0544 - val_loss: 0.0692 Epoch 942/1000 0s - loss: 0.0539 - val_loss: 0.0684 Epoch 943/1000 0s - loss: 0.0535 - val_loss: 0.0679 Epoch 944/1000 0s - loss: 0.0531 - val_loss: 0.0675 Epoch 945/1000 0s - loss: 0.0527 - val_loss: 0.0668 Epoch 946/1000 0s - loss: 0.0523 - val_loss: 0.0663 Epoch 947/1000 0s - loss: 0.0518 - val_loss: 0.0661 Epoch 948/1000 0s - loss: 0.0515 - val_loss: 0.0655 Epoch 949/1000 0s - loss: 0.0512 - val_loss: 0.0648 Epoch 950/1000 0s - loss: 0.0507 - val_loss: 0.0643 Epoch 951/1000 0s - loss: 0.0503 - val_loss: 0.0637 Epoch 952/1000 0s - loss: 0.0499 - val_loss: 0.0632 Epoch 953/1000 0s - loss: 0.0495 - val_loss: 0.0627 Epoch 954/1000 0s - loss: 0.0493 - val_loss: 0.0623 Epoch 955/1000 0s - loss: 0.0489 - val_loss: 0.0622 Epoch 956/1000 0s - loss: 0.0487 - val_loss: 0.0614 Epoch 957/1000 0s - loss: 0.0482 - val_loss: 0.0608 Epoch 958/1000 0s - loss: 0.0480 - val_loss: 0.0604 Epoch 959/1000 0s - loss: 0.0474 - val_loss: 0.0599 Epoch 960/1000 0s - loss: 0.0471 - val_loss: 0.0596 Epoch 961/1000 0s - loss: 0.0468 - val_loss: 0.0591 Epoch 962/1000 0s - loss: 0.0465 - val_loss: 0.0588 Epoch 963/1000 0s - loss: 0.0461 - val_loss: 0.0581 Epoch 964/1000 0s - loss: 0.0457 - val_loss: 0.0577 Epoch 965/1000 0s - loss: 0.0455 - val_loss: 0.0572 Epoch 966/1000 0s - loss: 0.0451 - val_loss: 0.0568 Epoch 967/1000 0s - loss: 0.0448 - val_loss: 0.0564 Epoch 968/1000 0s - loss: 0.0446 - val_loss: 0.0559 Epoch 969/1000 0s - loss: 0.0442 - val_loss: 0.0556 Epoch 970/1000 0s - loss: 0.0439 - val_loss: 0.0553 Epoch 971/1000 0s - loss: 0.0436 - val_loss: 0.0548 Epoch 972/1000 0s - loss: 0.0433 - val_loss: 0.0544 Epoch 973/1000 0s - loss: 0.0431 - val_loss: 0.0539 Epoch 974/1000 0s - loss: 0.0427 - val_loss: 0.0535 Epoch 975/1000 0s - loss: 0.0424 - val_loss: 0.0533 Epoch 976/1000 0s - loss: 0.0421 - val_loss: 0.0530 Epoch 977/1000 0s - loss: 0.0419 - val_loss: 0.0526 Epoch 978/1000 0s - loss: 0.0416 - val_loss: 0.0521 Epoch 979/1000 0s - loss: 0.0413 - val_loss: 0.0517 Epoch 980/1000 0s - loss: 0.0410 - val_loss: 0.0513 Epoch 981/1000 0s - loss: 0.0408 - val_loss: 0.0510 Epoch 982/1000 0s - loss: 0.0405 - val_loss: 0.0506 Epoch 983/1000 0s - loss: 0.0402 - val_loss: 0.0504 Epoch 984/1000 0s - loss: 0.0400 - val_loss: 0.0499 Epoch 985/1000 0s - loss: 0.0398 - val_loss: 0.0496 Epoch 986/1000 0s - loss: 0.0394 - val_loss: 0.0492 Epoch 987/1000 0s - loss: 0.0393 - val_loss: 0.0489 Epoch 988/1000 0s - loss: 0.0390 - val_loss: 0.0486 Epoch 989/1000 0s - loss: 0.0388 - val_loss: 0.0484 Epoch 990/1000 0s - loss: 0.0385 - val_loss: 0.0479 Epoch 991/1000 0s - loss: 0.0383 - val_loss: 0.0476 Epoch 992/1000 0s - loss: 0.0381 - val_loss: 0.0472 Epoch 993/1000 0s - loss: 0.0379 - val_loss: 0.0470 Epoch 994/1000 0s - loss: 0.0376 - val_loss: 0.0466 Epoch 995/1000 0s - loss: 0.0374 - val_loss: 0.0463 Epoch 996/1000 0s - loss: 0.0372 - val_loss: 0.0461 Epoch 997/1000 0s - loss: 0.0369 - val_loss: 0.0458 Epoch 998/1000 0s - loss: 0.0368 - val_loss: 0.0454 Epoch 999/1000 0s - loss: 0.0365 - val_loss: 0.0453 Epoch 1000/1000 0s - loss: 0.0363 - val_loss: 0.0448
predictY = model.predict(testX)
plt.subplot(121)
plt.plot(np.arange(len(history.history["loss"])), history.history["loss"], color="r", alpha=0.3, label="loss")
plt.plot(np.arange(len(history.history["val_loss"])), history.history["val_loss"], color="b", alpha=0.3, label="val_loss")
plt.subplot(122)
plt.plot(testX, predictY, "bo", alpha=0.3)
plt.plot(testX, testY, "ro", alpha=0.3)
[<matplotlib.lines.Line2D at 0x10ed8ddd0>]