Package: autopycoin
Welcome to AutopycoinThis is a deep learning package based on tensorflow.
The models are unofficial implementations.Available Modelsfromautopycoin.modelsimportcreate_interpretable_nbeats,create_interpretable_nbeats,NBEATS,PoolNBEATSModelepistemic errorAleotoric errorPaperNBEATSDropout or baggingQuantilesPaperAvailable Lossesfromautopycoin.lossesimportQuantileLossError,SymetricMeanAbsolutePercentageErrorLossesQuantileLosseErrorSymetricMeanAbsolutePercentageErrorDataset makerfromautopycoin.datasetimportWindowGeneratorDataset makerWindowGeneratorHow to use autopycoinpip install autopycoinHow to use AutopycoinThis notebook is a quick start for autopycoin. We will demonstrate its efficiency through a toy example.ImportAs our package is based on tensorflow we need to import it.importtensorflowastfLet's create our data thanks torandom_tswhich generate a fake time serie based on a trend and seasonality components.fromautopycoin.dataimportrandom_tstf.random.set_seed(0)data=random_ts(n_steps=400,# Number of steps (second dimension)trend_degree=2,periods=[10],# We can combine multiple periods, period is the time length for a cyclical function to reproduce a similar outputfourier_orders=[10],# higher is this number, more complex is the outputtrend_mean=0,trend_std=1,seasonality_mean=0,seasonality_std=1,batch_size=1,# Generate a batch of data (first dimension)n_variables=1,# Number of variables (last dimension)noise=True,# add normal centered noiseseed=42)denoised_data=random_ts(n_steps=400,# Number of steps (second dimension)trend_degree=2,periods=[10],# We can combine multiple periods, period is the time length for a cyclical function to reproduce a similar outputfourier_orders=[10],# higher is this number, more complex is the outputtrend_mean=0,trend_std=1,seasonality_mean=0,seasonality_std=1,batch_size=1,# Generate a batch of data (first dimension)n_variables=1,# Number of variables (last dimension)noise=False,# add normal centered noiseseed=42)# Let's render it with matplotlibimportmatplotlib.pyplotaspltplt.rcParams['figure.figsize']=(20,10)plt.plot(data[0],c='r',label='noised data')plt.plot(denoised_data[0],label='denoised data')plt.title('Generated data')plt.legend()<matplotlib.legend.Legend at 0x25a4597b040>Tensorflow libraryTensorflow usedatasetto feed its models. Hence, we create an objectWindowGeneratorto facilitate the creation of training, validation and training set.fromautopycoin.datasetimportWindowGeneratorWe first create an instance ofWindowgeneratorby defining important parameters value.
By defining aninput_widthof 70, we set our input length.label_widthset our label length.shiftrepresents the shift between label and input data. Hence if you don't want label and input overlappingshifthas to be equal tolabel_width. It can help for more complex model like lstm models which can be trained to reconstruct their inputs.test_sizeandvalid_sizeare the numbers of examples to predict. If they are integer, they are the true number of examples, if they are float then it's a proportion of the entire data.Note: We create lot of examples by shifting by one the window of prediction.w=WindowGenerator(input_width=80,label_width=40,shift=40,test_size=50,valid_size=10,flat=True,batch_size=16,preprocessing=lambdax,y:(x,(x,y))# NBEATS output)We can't use it now as it needs to be initialized by an array, tensor or a dataframew=w.from_array(data=data[0],# Has to be 2D arrayinput_columns=[0],known_columns=[],label_columns=[0],date_columns=[],)# Or using dataframeimportpandasaspddata=pd.DataFrame(data.numpy()[0],columns=['test'])w=w.from_array(data=data,# Has to be 2D arrayinput_columns=['test'],known_columns=[],label_columns=['test'],date_columns=[],)w.train<BatchDataset element_spec=(TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), (TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), TensorSpec(shape=(None, 40), dtype=tf.float32, name=None)))>w.valid<BatchDataset element_spec=(TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), (TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), TensorSpec(shape=(None, 40), dtype=tf.float32, name=None)))>w.test<BatchDataset element_spec=(TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), (TensorSpec(shape=(None, 80), dtype=tf.float32, name=None), TensorSpec(shape=(None, 40), dtype=tf.float32, name=None)))>It's time for inferenceWe import the nbeats module where the nbdeats model is defined.There are currently multiple version of nbeats implemented ascreate_interpretable_nbeatsorcreate_generic_nbeats.
But it is possible to customize it's own nbeats model by subclassing the nbeats Model and Layer.fromautopycoin.modelsimportnbeatsmodel1=nbeats.create_interpretable_nbeats(label_width=40,forecast_periods=[10],backcast_periods=[10],forecast_fourier_order=[10],backcast_fourier_order=[10],p_degree=1,trend_n_neurons=200,seasonality_n_neurons=200,drop_rate=0.,share=True)model1.compile(tf.keras.optimizers.Adam(learning_rate=0.0015,beta_1=0.9,beta_2=0.999,epsilon=1e-07,amsgrad=True,name='Adam'),loss='mse',loss_weights=[1,1],# In the paper = [0, 1]metrics=["mae"])model1.fit(w.train,validation_data=w.valid,epochs=20)Epoch 1/20
9/9 [==============================] - 10s 264ms/step - loss: 107.7595 - output_1_loss: 48.8508 - output_2_loss: 58.9087 - output_1_mae: 4.6331 - output_2_mae: 5.6065 - val_loss: 28.2610 - val_output_1_loss: 6.3400 - val_output_2_loss: 21.9210 - val_output_1_mae: 2.0182 - val_output_2_mae: 3.4445
Epoch 2/20
9/9 [==============================] - 0s 28ms/step - loss: 17.0173 - output_1_loss: 5.0084 - output_2_loss: 12.0089 - output_1_mae: 1.7634 - output_2_mae: 2.6280 - val_loss: 9.0327 - val_output_1_loss: 3.2561 - val_output_2_loss: 5.7766 - val_output_1_mae: 1.4348 - val_output_2_mae: 1.9232
Epoch 3/20
9/9 [==============================] - 0s 31ms/step - loss: 7.7175 - output_1_loss: 2.6491 - output_2_loss: 5.0685 - output_1_mae: 1.2985 - output_2_mae: 1.7502 - val_loss: 4.7811 - val_output_1_loss: 1.9094 - val_output_2_loss: 2.8717 - val_output_1_mae: 1.1005 - val_output_2_mae: 1.3431
Epoch 4/20
9/9 [==============================] - 0s 31ms/step - loss: 4.4976 - output_1_loss: 1.9064 - output_2_loss: 2.5912 - output_1_mae: 1.1020 - output_2_mae: 1.2652 - val_loss: 4.1069 - val_output_1_loss: 1.5517 - val_output_2_loss: 2.5552 - val_output_1_mae: 0.9765 - val_output_2_mae: 1.3012
Epoch 5/20
9/9 [==============================] - 0s 30ms/step - loss: 3.3197 - output_1_loss: 1.4996 - output_2_loss: 1.8201 - output_1_mae: 0.9801 - output_2_mae: 1.0888 - val_loss: 3.4868 - val_output_1_loss: 1.3489 - val_output_2_loss: 2.1379 - val_output_1_mae: 0.9232 - val_output_2_mae: 1.1573
Epoch 6/20
9/9 [==============================] - 0s 31ms/step - loss: 2.8041 - output_1_loss: 1.3137 - output_2_loss: 1.4904 - output_1_mae: 0.9224 - output_2_mae: 0.9713 - val_loss: 3.0279 - val_output_1_loss: 1.1370 - val_output_2_loss: 1.8908 - val_output_1_mae: 0.8540 - val_output_2_mae: 1.0975
Epoch 7/20
9/9 [==============================] - 0s 31ms/step - loss: 2.5440 - output_1_loss: 1.2236 - output_2_loss: 1.3204 - output_1_mae: 0.8918 - output_2_mae: 0.9265 - val_loss: 2.9015 - val_output_1_loss: 1.0745 - val_output_2_loss: 1.8270 - val_output_1_mae: 0.8283 - val_output_2_mae: 1.0760
Epoch 8/20
9/9 [==============================] - 0s 30ms/step - loss: 2.4074 - output_1_loss: 1.1735 - output_2_loss: 1.2339 - output_1_mae: 0.8744 - output_2_mae: 0.8928 - val_loss: 2.8078 - val_output_1_loss: 1.0035 - val_output_2_loss: 1.8042 - val_output_1_mae: 0.8019 - val_output_2_mae: 1.0765
Epoch 9/20
9/9 [==============================] - 0s 33ms/step - loss: 2.2903 - output_1_loss: 1.1395 - output_2_loss: 1.1508 - output_1_mae: 0.8615 - output_2_mae: 0.8617 - val_loss: 2.7515 - val_output_1_loss: 0.9931 - val_output_2_loss: 1.7584 - val_output_1_mae: 0.7983 - val_output_2_mae: 1.0654
Epoch 10/20
9/9 [==============================] - 0s 42ms/step - loss: 2.2125 - output_1_loss: 1.1157 - output_2_loss: 1.0968 - output_1_mae: 0.8529 - output_2_mae: 0.8449 - val_loss: 2.7506 - val_output_1_loss: 0.9789 - val_output_2_loss: 1.7718 - val_output_1_mae: 0.7917 - val_output_2_mae: 1.0708
Epoch 11/20
9/9 [==============================] - 0s 34ms/step - loss: 2.1483 - output_1_loss: 1.0973 - output_2_loss: 1.0510 - output_1_mae: 0.8459 - output_2_mae: 0.8261 - val_loss: 2.6904 - val_output_1_loss: 0.9565 - val_output_2_loss: 1.7339 - val_output_1_mae: 0.7839 - val_output_2_mae: 1.0580
Epoch 12/20
9/9 [==============================] - 0s 31ms/step - loss: 2.0970 - output_1_loss: 1.0808 - output_2_loss: 1.0162 - output_1_mae: 0.8401 - output_2_mae: 0.8130 - val_loss: 2.6640 - val_output_1_loss: 0.9480 - val_output_2_loss: 1.7161 - val_output_1_mae: 0.7820 - val_output_2_mae: 1.0537
Epoch 13/20
9/9 [==============================] - 0s 29ms/step - loss: 2.0540 - output_1_loss: 1.0680 - output_2_loss: 0.9860 - output_1_mae: 0.8356 - output_2_mae: 0.8008 - val_loss: 2.6468 - val_output_1_loss: 0.9326 - val_output_2_loss: 1.7142 - val_output_1_mae: 0.7754 - val_output_2_mae: 1.0538
Epoch 14/20
9/9 [==============================] - 0s 29ms/step - loss: 2.0171 - output_1_loss: 1.0562 - output_2_loss: 0.9609 - output_1_mae: 0.8310 - output_2_mae: 0.7910 - val_loss: 2.6439 - val_output_1_loss: 0.9350 - val_output_2_loss: 1.7090 - val_output_1_mae: 0.7752 - val_output_2_mae: 1.0473
Epoch 15/20
9/9 [==============================] - 0s 29ms/step - loss: 1.9864 - output_1_loss: 1.0468 - output_2_loss: 0.9396 - output_1_mae: 0.8273 - output_2_mae: 0.7809 - val_loss: 2.6282 - val_output_1_loss: 0.9284 - val_output_2_loss: 1.6998 - val_output_1_mae: 0.7728 - val_output_2_mae: 1.0454
Epoch 16/20
9/9 [==============================] - 0s 30ms/step - loss: 1.9587 - output_1_loss: 1.0372 - output_2_loss: 0.9215 - output_1_mae: 0.8237 - output_2_mae: 0.7729 - val_loss: 2.6171 - val_output_1_loss: 0.9228 - val_output_2_loss: 1.6943 - val_output_1_mae: 0.7705 - val_output_2_mae: 1.0443
Epoch 17/20
9/9 [==============================] - 0s 36ms/step - loss: 1.9331 - output_1_loss: 1.0287 - output_2_loss: 0.9045 - output_1_mae: 0.8206 - output_2_mae: 0.7672 - val_loss: 2.6150 - val_output_1_loss: 0.9207 - val_output_2_loss: 1.6943 - val_output_1_mae: 0.7690 - val_output_2_mae: 1.0435
Epoch 18/20
9/9 [==============================] - 0s 31ms/step - loss: 1.9092 - output_1_loss: 1.0210 - output_2_loss: 0.8882 - output_1_mae: 0.8180 - output_2_mae: 0.7597 - val_loss: 2.5927 - val_output_1_loss: 0.9081 - val_output_2_loss: 1.6846 - val_output_1_mae: 0.7642 - val_output_2_mae: 1.0409
Epoch 19/20
9/9 [==============================] - 0s 30ms/step - loss: 1.8908 - output_1_loss: 1.0147 - output_2_loss: 0.8761 - output_1_mae: 0.8155 - output_2_mae: 0.7552 - val_loss: 2.6052 - val_output_1_loss: 0.9144 - val_output_2_loss: 1.6908 - val_output_1_mae: 0.7666 - val_output_2_mae: 1.0435
Epoch 20/20
9/9 [==============================] - 0s 31ms/step - loss: 1.8719 - output_1_loss: 1.0081 - output_2_loss: 0.8638 - output_1_mae: 0.8130 - output_2_mae: 0.7485 - val_loss: 2.6011 - val_output_1_loss: 0.9084 - val_output_2_loss: 1.6927 - val_output_1_mae: 0.7635 - val_output_2_mae: 1.0453





<keras.callbacks.History at 0x25a4268de20>fromautopycoin.modelsimportnbeatsmodel2=nbeats.create_generic_nbeats(label_width=40,g_forecast_neurons=16,g_backcast_neurons=16,n_neurons=16,n_blocks=3,n_stacks=2,drop_rate=0.1,share=True)model2.compile(tf.keras.optimizers.Adam(learning_rate=0.0015,beta_1=0.9,beta_2=0.999,epsilon=1e-07,amsgrad=True,name='Adam'),loss='mse',loss_weights=[1,1],# In the paper = [0, 1]metrics=["mae"])model2.fit(w.train,validation_data=w.valid,epochs=20)Epoch 1/20
9/9 [==============================] - 10s 273ms/step - loss: 42.6128 - output_1_loss: 20.5989 - output_2_loss: 22.0138 - output_1_mae: 2.9896 - output_2_mae: 3.3102 - val_loss: 35.9356 - val_output_1_loss: 17.8830 - val_output_2_loss: 18.0526 - val_output_1_mae: 2.7465 - val_output_2_mae: 2.8812
Epoch 2/20
9/9 [==============================] - 0s 34ms/step - loss: 33.6867 - output_1_loss: 16.9436 - output_2_loss: 16.7431 - output_1_mae: 2.5379 - output_2_mae: 2.6609 - val_loss: 32.3713 - val_output_1_loss: 16.0248 - val_output_2_loss: 16.3464 - val_output_1_mae: 2.5090 - val_output_2_mae: 2.4740
Epoch 3/20
9/9 [==============================] - 0s 35ms/step - loss: 31.6279 - output_1_loss: 16.0887 - output_2_loss: 15.5393 - output_1_mae: 2.4137 - output_2_mae: 2.4602 - val_loss: 31.0422 - val_output_1_loss: 15.9003 - val_output_2_loss: 15.1419 - val_output_1_mae: 2.4889 - val_output_2_mae: 2.3757
Epoch 4/20
9/9 [==============================] - 0s 38ms/step - loss: 30.7158 - output_1_loss: 15.7325 - output_2_loss: 14.9833 - output_1_mae: 2.3807 - output_2_mae: 2.4034 - val_loss: 30.2672 - val_output_1_loss: 15.5197 - val_output_2_loss: 14.7476 - val_output_1_mae: 2.4714 - val_output_2_mae: 2.3704
Epoch 5/20
9/9 [==============================] - 0s 36ms/step - loss: 29.9507 - output_1_loss: 15.4581 - output_2_loss: 14.4926 - output_1_mae: 2.3601 - output_2_mae: 2.3899 - val_loss: 29.1492 - val_output_1_loss: 15.3752 - val_output_2_loss: 13.7740 - val_output_1_mae: 2.4536 - val_output_2_mae: 2.3348
Epoch 6/20
9/9 [==============================] - 0s 37ms/step - loss: 28.9890 - output_1_loss: 15.2142 - output_2_loss: 13.7748 - output_1_mae: 2.3620 - output_2_mae: 2.3576 - val_loss: 27.7325 - val_output_1_loss: 14.7231 - val_output_2_loss: 13.0094 - val_output_1_mae: 2.4348 - val_output_2_mae: 2.3628
Epoch 7/20
9/9 [==============================] - 0s 37ms/step - loss: 27.9566 - output_1_loss: 14.8224 - output_2_loss: 13.1342 - output_1_mae: 2.3521 - output_2_mae: 2.3442 - val_loss: 27.0617 - val_output_1_loss: 14.4031 - val_output_2_loss: 12.6587 - val_output_1_mae: 2.4278 - val_output_2_mae: 2.3942
Epoch 8/20
9/9 [==============================] - 0s 38ms/step - loss: 26.8509 - output_1_loss: 14.4216 - output_2_loss: 12.4294 - output_1_mae: 2.3607 - output_2_mae: 2.3420 - val_loss: 26.5709 - val_output_1_loss: 14.1705 - val_output_2_loss: 12.4004 - val_output_1_mae: 2.4366 - val_output_2_mae: 2.4046
Epoch 9/20
9/9 [==============================] - 0s 45ms/step - loss: 25.5537 - output_1_loss: 13.9055 - output_2_loss: 11.6482 - output_1_mae: 2.3517 - output_2_mae: 2.2923 - val_loss: 25.4198 - val_output_1_loss: 13.6695 - val_output_2_loss: 11.7503 - val_output_1_mae: 2.4041 - val_output_2_mae: 2.2838
Epoch 10/20
9/9 [==============================] - 0s 39ms/step - loss: 24.2401 - output_1_loss: 13.3442 - output_2_loss: 10.8959 - output_1_mae: 2.3349 - output_2_mae: 2.2453 - val_loss: 24.0776 - val_output_1_loss: 12.8328 - val_output_2_loss: 11.2448 - val_output_1_mae: 2.4019 - val_output_2_mae: 2.3412
Epoch 11/20
9/9 [==============================] - 0s 39ms/step - loss: 22.8726 - output_1_loss: 12.5804 - output_2_loss: 10.2922 - output_1_mae: 2.3036 - output_2_mae: 2.2185 - val_loss: 22.0797 - val_output_1_loss: 11.8361 - val_output_2_loss: 10.2435 - val_output_1_mae: 2.3351 - val_output_2_mae: 2.2578
Epoch 12/20
9/9 [==============================] - 0s 37ms/step - loss: 21.3568 - output_1_loss: 11.8595 - output_2_loss: 9.4973 - output_1_mae: 2.2715 - output_2_mae: 2.1294 - val_loss: 20.7301 - val_output_1_loss: 11.2053 - val_output_2_loss: 9.5248 - val_output_1_mae: 2.2682 - val_output_2_mae: 2.1940
Epoch 13/20
9/9 [==============================] - 0s 37ms/step - loss: 19.9969 - output_1_loss: 10.9618 - output_2_loss: 9.0351 - output_1_mae: 2.2187 - output_2_mae: 2.1088 - val_loss: 19.5411 - val_output_1_loss: 10.4076 - val_output_2_loss: 9.1335 - val_output_1_mae: 2.2347 - val_output_2_mae: 2.1147
Epoch 14/20
9/9 [==============================] - 0s 39ms/step - loss: 18.2909 - output_1_loss: 10.0314 - output_2_loss: 8.2595 - output_1_mae: 2.1757 - output_2_mae: 2.0099 - val_loss: 17.4031 - val_output_1_loss: 9.2104 - val_output_2_loss: 8.1927 - val_output_1_mae: 2.1068 - val_output_2_mae: 1.9743
Epoch 15/20
9/9 [==============================] - 0s 38ms/step - loss: 16.9036 - output_1_loss: 9.1755 - output_2_loss: 7.7281 - output_1_mae: 2.1134 - output_2_mae: 1.9526 - val_loss: 15.8572 - val_output_1_loss: 8.1932 - val_output_2_loss: 7.6641 - val_output_1_mae: 2.0244 - val_output_2_mae: 1.9575
Epoch 16/20
9/9 [==============================] - 0s 37ms/step - loss: 15.6807 - output_1_loss: 8.4435 - output_2_loss: 7.2372 - output_1_mae: 2.0389 - output_2_mae: 1.8990 - val_loss: 15.5640 - val_output_1_loss: 8.1138 - val_output_2_loss: 7.4503 - val_output_1_mae: 2.0440 - val_output_2_mae: 1.9175
Epoch 17/20
9/9 [==============================] - 0s 39ms/step - loss: 14.5375 - output_1_loss: 7.7209 - output_2_loss: 6.8166 - output_1_mae: 1.9561 - output_2_mae: 1.8343 - val_loss: 14.9077 - val_output_1_loss: 7.8515 - val_output_2_loss: 7.0563 - val_output_1_mae: 2.0159 - val_output_2_mae: 1.8616
Epoch 18/20
9/9 [==============================] - 0s 39ms/step - loss: 13.4057 - output_1_loss: 7.0658 - output_2_loss: 6.3399 - output_1_mae: 1.9002 - output_2_mae: 1.7726 - val_loss: 12.9685 - val_output_1_loss: 6.5909 - val_output_2_loss: 6.3776 - val_output_1_mae: 1.8825 - val_output_2_mae: 1.8605
Epoch 19/20
9/9 [==============================] - 0s 37ms/step - loss: 11.6256 - output_1_loss: 6.0771 - output_2_loss: 5.5485 - output_1_mae: 1.7945 - output_2_mae: 1.6762 - val_loss: 11.7937 - val_output_1_loss: 6.1221 - val_output_2_loss: 5.6716 - val_output_1_mae: 1.8922 - val_output_2_mae: 1.7786
Epoch 20/20
9/9 [==============================] - 0s 39ms/step - loss: 11.2368 - output_1_loss: 5.8081 - output_2_loss: 5.4287 - output_1_mae: 1.7738 - output_2_mae: 1.6614 - val_loss: 10.8702 - val_output_1_loss: 5.4575 - val_output_2_loss: 5.4126 - val_output_1_mae: 1.7811 - val_output_2_mae: 1.7010





<keras.callbacks.History at 0x25a4c7eb3d0>Evaluationmodel1.evaluate(w.test)4/4 [==============================] - 0s 15ms/step - loss: 2.6927 - output_1_loss: 1.2548 - output_2_loss: 1.4379 - output_1_mae: 0.8934 - output_2_mae: 0.9630





[2.692697048187256,
 1.2548267841339111,
 1.4378703832626343,
 0.8934133052825928,
 0.9630153179168701]model2.evaluate(w.test)4/4 [==============================] - 0s 20ms/step - loss: 10.9378 - output_1_loss: 5.5514 - output_2_loss: 5.3864 - output_1_mae: 1.7609 - output_2_mae: 1.6672





[10.937788963317871,
 5.551424503326416,
 5.386363506317139,
 1.7609360218048096,
 1.66715669631958]PlotLet plot some previsionsimportmatplotlib.pyplotaspltiterator=iter(w.train)x,y=iterator.get_next()input_width=80plt.plot(range(input_width,input_width+40),model1.predict(x)[1].values[0],label='forecast')# Usefull only if stack = Trueplt.plot(range(input_width),model1.predict(x)[0].values[0],label='backcast')plt.plot(range(input_width,input_width+40),y[1][0],label='labels')plt.plot(range(input_width),x[0],label='inputs')plt.legend()<matplotlib.legend.Legend at 0x25a5293e4f0>Productionprod=w.production(data)plt.plot(model1.predict(prod)[0].values[1])[<matplotlib.lines.Line2D at 0x25a5363f2b0>]A pool is better than a single modelGenerally, a pool is better than a single model : It is a regularization method.
In this toy example, it won't because we are not optimizing the training phase.fromautopycoin.modelsimportPoolNBEATS# Create a callable which define label_width and create a Tensorflow Model.model=lambdalabel_width:nbeats.create_interpretable_nbeats(label_width=label_width,forecast_periods=[10],backcast_periods=[10],forecast_fourier_order=[10],backcast_fourier_order=[10],p_degree=1,trend_n_neurons=200,seasonality_n_neurons=200,drop_rate=0.,share=True)# Define an aggregation method with fn_agg, the number of models with n_models and the# label width with label_width. label_width is mandatory if you use callables in nbeats_models !model=PoolNBEATS(n_models=3,label_width=40,nbeats_models=model,fn_agg=tf.reduce_mean)model.compile(tf.keras.optimizers.Adam(learning_rate=0.001,beta_1=0.9,beta_2=0.999,epsilon=1e-07,amsgrad=True,name='Adam'),loss=['mse','mse'],metrics=['mae'])# The fit is printing 6 different maes results: 2 for each model (backcast, forecast).# The loss is aggregated then it is printing 4 loss results: 1 for the entire model,model.fit(w.train,validation_data=w.valid,epochs=20)Epoch 1/20
9/9 [==============================] - 43s 829ms/step - loss: 129.9779 - output_1_1_loss: 54.8111 - output_1_2_loss: 58.6071 - output_2_1_loss: 16.5597 - output_1_1_mae: 5.1632 - output_1_2_mae: 5.4097 - output_2_1_mae: 3.1162 - output_2_2_mae: 3.6870 - output_3_1_mae: 4.8781 - output_3_2_mae: 4.5370 - val_loss: 46.9568 - val_output_1_1_loss: 14.0975 - val_output_1_2_loss: 24.6893 - val_output_2_1_loss: 8.1700 - val_output_1_1_mae: 2.9376 - val_output_1_2_mae: 3.7093 - val_output_2_1_mae: 2.3219 - val_output_2_2_mae: 2.5332 - val_output_3_1_mae: 3.8348 - val_output_3_2_mae: 3.3322
Epoch 2/20
9/9 [==============================] - 1s 74ms/step - loss: 25.4214 - output_1_1_loss: 7.0674 - output_1_2_loss: 14.4327 - output_2_1_loss: 3.9213 - output_1_1_mae: 2.0938 - output_1_2_mae: 2.8991 - output_2_1_mae: 1.5464 - output_2_2_mae: 1.9865 - output_3_1_mae: 2.3966 - output_3_2_mae: 2.8036 - val_loss: 17.6809 - val_output_1_1_loss: 4.1897 - val_output_1_2_loss: 11.3001 - val_output_2_1_loss: 2.1912 - val_output_1_1_mae: 1.6525 - val_output_1_2_mae: 2.6448 - val_output_2_1_mae: 1.1933 - val_output_2_2_mae: 1.8943 - val_output_3_1_mae: 1.8263 - val_output_3_2_mae: 2.2586
Epoch 3/20
9/9 [==============================] - 1s 86ms/step - loss: 11.8408 - output_1_1_loss: 3.4665 - output_1_2_loss: 6.3762 - output_2_1_loss: 1.9981 - output_1_1_mae: 1.4820 - output_1_2_mae: 1.9251 - output_2_1_mae: 1.1135 - output_2_2_mae: 1.4251 - output_3_1_mae: 1.5350 - output_3_2_mae: 1.9391 - val_loss: 8.2325 - val_output_1_1_loss: 2.1980 - val_output_1_2_loss: 4.5575 - val_output_2_1_loss: 1.4771 - val_output_1_1_mae: 1.2003 - val_output_1_2_mae: 1.7161 - val_output_2_1_mae: 0.9559 - val_output_2_2_mae: 1.8211 - val_output_3_1_mae: 1.4079 - val_output_3_2_mae: 1.7984
Epoch 4/20
9/9 [==============================] - 1s 90ms/step - loss: 6.9427 - output_1_1_loss: 2.2510 - output_1_2_loss: 3.3005 - output_2_1_loss: 1.3912 - output_1_1_mae: 1.2012 - output_1_2_mae: 1.4370 - output_2_1_mae: 0.9312 - output_2_2_mae: 1.2682 - output_3_1_mae: 1.2522 - output_3_2_mae: 1.3777 - val_loss: 5.8157 - val_output_1_1_loss: 1.7247 - val_output_1_2_loss: 3.0949 - val_output_2_1_loss: 0.9962 - val_output_1_1_mae: 1.0654 - val_output_1_2_mae: 1.3998 - val_output_2_1_mae: 0.7977 - val_output_2_2_mae: 1.6829 - val_output_3_1_mae: 1.1519 - val_output_3_2_mae: 1.4012
Epoch 5/20
9/9 [==============================] - 1s 86ms/step - loss: 5.0599 - output_1_1_loss: 1.7671 - output_1_2_loss: 2.1756 - output_2_1_loss: 1.1172 - output_1_1_mae: 1.0628 - output_1_2_mae: 1.1825 - output_2_1_mae: 0.8378 - output_2_2_mae: 1.4185 - output_3_1_mae: 1.0667 - output_3_2_mae: 1.1119 - val_loss: 4.7978 - val_output_1_1_loss: 1.3100 - val_output_1_2_loss: 2.6150 - val_output_2_1_loss: 0.8728 - val_output_1_1_mae: 0.9288 - val_output_1_2_mae: 1.3082 - val_output_2_1_mae: 0.7442 - val_output_2_2_mae: 1.0579 - val_output_3_1_mae: 0.9992 - val_output_3_2_mae: 1.2378
Epoch 6/20
9/9 [==============================] - 1s 87ms/step - loss: 4.2656 - output_1_1_loss: 1.4522 - output_1_2_loss: 1.7817 - output_2_1_loss: 1.0316 - output_1_1_mae: 0.9619 - output_1_2_mae: 1.0819 - output_2_1_mae: 0.8059 - output_2_2_mae: 1.3582 - output_3_1_mae: 0.9386 - output_3_2_mae: 1.0177 - val_loss: 3.9572 - val_output_1_1_loss: 1.1128 - val_output_1_2_loss: 2.0426 - val_output_2_1_loss: 0.8018 - val_output_1_1_mae: 0.8523 - val_output_1_2_mae: 1.1461 - val_output_2_1_mae: 0.7204 - val_output_2_2_mae: 1.6967 - val_output_3_1_mae: 0.8754 - val_output_3_2_mae: 1.1660
Epoch 7/20
9/9 [==============================] - 1s 100ms/step - loss: 3.8616 - output_1_1_loss: 1.3152 - output_1_2_loss: 1.5499 - output_2_1_loss: 0.9965 - output_1_1_mae: 0.9157 - output_1_2_mae: 1.0036 - output_2_1_mae: 0.7941 - output_2_2_mae: 1.1896 - output_3_1_mae: 0.8731 - output_3_2_mae: 0.9514 - val_loss: 3.8208 - val_output_1_1_loss: 1.0152 - val_output_1_2_loss: 2.0275 - val_output_2_1_loss: 0.7781 - val_output_1_1_mae: 0.8203 - val_output_1_2_mae: 1.1385 - val_output_2_1_mae: 0.7071 - val_output_2_2_mae: 1.2886 - val_output_3_1_mae: 0.8086 - val_output_3_2_mae: 1.1600
Epoch 8/20
9/9 [==============================] - 1s 102ms/step - loss: 3.5669 - output_1_1_loss: 1.2355 - output_1_2_loss: 1.3619 - output_2_1_loss: 0.9696 - output_1_1_mae: 0.8884 - output_1_2_mae: 0.9339 - output_2_1_mae: 0.7838 - output_2_2_mae: 0.9751 - output_3_1_mae: 0.8383 - output_3_2_mae: 0.9076 - val_loss: 3.5757 - val_output_1_1_loss: 0.9730 - val_output_1_2_loss: 1.8360 - val_output_2_1_loss: 0.7667 - val_output_1_1_mae: 0.8063 - val_output_1_2_mae: 1.0796 - val_output_2_1_mae: 0.7042 - val_output_2_2_mae: 1.0481 - val_output_3_1_mae: 0.7874 - val_output_3_2_mae: 1.1607
Epoch 9/20
9/9 [==============================] - 1s 90ms/step - loss: 3.3985 - output_1_1_loss: 1.1943 - output_1_2_loss: 1.2635 - output_2_1_loss: 0.9407 - output_1_1_mae: 0.8756 - output_1_2_mae: 0.8978 - output_2_1_mae: 0.7717 - output_2_2_mae: 0.8747 - output_3_1_mae: 0.8182 - output_3_2_mae: 0.8798 - val_loss: 3.6140 - val_output_1_1_loss: 0.9473 - val_output_1_2_loss: 1.9000 - val_output_2_1_loss: 0.7666 - val_output_1_1_mae: 0.7956 - val_output_1_2_mae: 1.0952 - val_output_2_1_mae: 0.7048 - val_output_2_2_mae: 1.0705 - val_output_3_1_mae: 0.7708 - val_output_3_2_mae: 1.1711
Epoch 10/20
9/9 [==============================] - 1s 88ms/step - loss: 3.2628 - output_1_1_loss: 1.1613 - output_1_2_loss: 1.1828 - output_2_1_loss: 0.9187 - output_1_1_mae: 0.8640 - output_1_2_mae: 0.8752 - output_2_1_mae: 0.7643 - output_2_2_mae: 0.8340 - output_3_1_mae: 0.8036 - output_3_2_mae: 0.8597 - val_loss: 3.6254 - val_output_1_1_loss: 0.9419 - val_output_1_2_loss: 1.9278 - val_output_2_1_loss: 0.7556 - val_output_1_1_mae: 0.7926 - val_output_1_2_mae: 1.1058 - val_output_2_1_mae: 0.7003 - val_output_2_2_mae: 1.0737 - val_output_3_1_mae: 0.7666 - val_output_3_2_mae: 1.1687
Epoch 11/20
9/9 [==============================] - 1s 86ms/step - loss: 3.1657 - output_1_1_loss: 1.1376 - output_1_2_loss: 1.1247 - output_2_1_loss: 0.9034 - output_1_1_mae: 0.8553 - output_1_2_mae: 0.8621 - output_2_1_mae: 0.7592 - output_2_2_mae: 0.8126 - output_3_1_mae: 0.7948 - output_3_2_mae: 0.8450 - val_loss: 3.5362 - val_output_1_1_loss: 0.9206 - val_output_1_2_loss: 1.8659 - val_output_2_1_loss: 0.7497 - val_output_1_1_mae: 0.7823 - val_output_1_2_mae: 1.0899 - val_output_2_1_mae: 0.6958 - val_output_2_2_mae: 1.0339 - val_output_3_1_mae: 0.7653 - val_output_3_2_mae: 1.1516
Epoch 12/20
9/9 [==============================] - 1s 87ms/step - loss: 3.1066 - output_1_1_loss: 1.1205 - output_1_2_loss: 1.0941 - output_2_1_loss: 0.8920 - output_1_1_mae: 0.8487 - output_1_2_mae: 0.8511 - output_2_1_mae: 0.7547 - output_2_2_mae: 0.7885 - output_3_1_mae: 0.7868 - output_3_2_mae: 0.8316 - val_loss: 3.4825 - val_output_1_1_loss: 0.9152 - val_output_1_2_loss: 1.8193 - val_output_2_1_loss: 0.7479 - val_output_1_1_mae: 0.7806 - val_output_1_2_mae: 1.0748 - val_output_2_1_mae: 0.6967 - val_output_2_2_mae: 1.0604 - val_output_3_1_mae: 0.7619 - val_output_3_2_mae: 1.1315
Epoch 13/20
9/9 [==============================] - 1s 89ms/step - loss: 3.0458 - output_1_1_loss: 1.1044 - output_1_2_loss: 1.0592 - output_2_1_loss: 0.8822 - output_1_1_mae: 0.8432 - output_1_2_mae: 0.8309 - output_2_1_mae: 0.7507 - output_2_2_mae: 0.7824 - output_3_1_mae: 0.7802 - output_3_2_mae: 0.8183 - val_loss: 3.5015 - val_output_1_1_loss: 0.9050 - val_output_1_2_loss: 1.8511 - val_output_2_1_loss: 0.7454 - val_output_1_1_mae: 0.7771 - val_output_1_2_mae: 1.0792 - val_output_2_1_mae: 0.6925 - val_output_2_2_mae: 1.0433 - val_output_3_1_mae: 0.7592 - val_output_3_2_mae: 1.1145
Epoch 14/20
9/9 [==============================] - 1s 88ms/step - loss: 2.9937 - output_1_1_loss: 1.0914 - output_1_2_loss: 1.0284 - output_2_1_loss: 0.8740 - output_1_1_mae: 0.8393 - output_1_2_mae: 0.8118 - output_2_1_mae: 0.7473 - output_2_2_mae: 0.7690 - output_3_1_mae: 0.7750 - output_3_2_mae: 0.8043 - val_loss: 3.4499 - val_output_1_1_loss: 0.9009 - val_output_1_2_loss: 1.8061 - val_output_2_1_loss: 0.7429 - val_output_1_1_mae: 0.7749 - val_output_1_2_mae: 1.0674 - val_output_2_1_mae: 0.6918 - val_output_2_2_mae: 1.0505 - val_output_3_1_mae: 0.7601 - val_output_3_2_mae: 1.1067
Epoch 15/20
9/9 [==============================] - 1s 89ms/step - loss: 2.9859 - output_1_1_loss: 1.0803 - output_1_2_loss: 1.0397 - output_2_1_loss: 0.8658 - output_1_1_mae: 0.8357 - output_1_2_mae: 0.8106 - output_2_1_mae: 0.7445 - output_2_2_mae: 0.7634 - output_3_1_mae: 0.7699 - output_3_2_mae: 0.7922 - val_loss: 3.4064 - val_output_1_1_loss: 0.8995 - val_output_1_2_loss: 1.7671 - val_output_2_1_loss: 0.7399 - val_output_1_1_mae: 0.7744 - val_output_1_2_mae: 1.0629 - val_output_2_1_mae: 0.6911 - val_output_2_2_mae: 1.0509 - val_output_3_1_mae: 0.7584 - val_output_3_2_mae: 1.1074
Epoch 16/20
9/9 [==============================] - 1s 86ms/step - loss: 2.9870 - output_1_1_loss: 1.0734 - output_1_2_loss: 1.0549 - output_2_1_loss: 0.8587 - output_1_1_mae: 0.8338 - output_1_2_mae: 0.8185 - output_2_1_mae: 0.7419 - output_2_2_mae: 0.7561 - output_3_1_mae: 0.7658 - output_3_2_mae: 0.7829 - val_loss: 3.6377 - val_output_1_1_loss: 0.9016 - val_output_1_2_loss: 2.0002 - val_output_2_1_loss: 0.7359 - val_output_1_1_mae: 0.7739 - val_output_1_2_mae: 1.1345 - val_output_2_1_mae: 0.6889 - val_output_2_2_mae: 1.0411 - val_output_3_1_mae: 0.7554 - val_output_3_2_mae: 1.1253
Epoch 17/20
9/9 [==============================] - 1s 90ms/step - loss: 2.9628 - output_1_1_loss: 1.0636 - output_1_2_loss: 1.0463 - output_2_1_loss: 0.8529 - output_1_1_mae: 0.8296 - output_1_2_mae: 0.8272 - output_2_1_mae: 0.7396 - output_2_2_mae: 0.7494 - output_3_1_mae: 0.7621 - output_3_2_mae: 0.7780 - val_loss: 3.8666 - val_output_1_1_loss: 0.9005 - val_output_1_2_loss: 2.2291 - val_output_2_1_loss: 0.7369 - val_output_1_1_mae: 0.7731 - val_output_1_2_mae: 1.2023 - val_output_2_1_mae: 0.6899 - val_output_2_2_mae: 1.0530 - val_output_3_1_mae: 0.7531 - val_output_3_2_mae: 1.1594
Epoch 18/20
9/9 [==============================] - 1s 93ms/step - loss: 3.0166 - output_1_1_loss: 1.0595 - output_1_2_loss: 1.1103 - output_2_1_loss: 0.8468 - output_1_1_mae: 0.8277 - output_1_2_mae: 0.8641 - output_2_1_mae: 0.7373 - output_2_2_mae: 0.7442 - output_3_1_mae: 0.7590 - output_3_2_mae: 0.7782 - val_loss: 3.4706 - val_output_1_1_loss: 0.8868 - val_output_1_2_loss: 1.8534 - val_output_2_1_loss: 0.7304 - val_output_1_1_mae: 0.7681 - val_output_1_2_mae: 1.0926 - val_output_2_1_mae: 0.6866 - val_output_2_2_mae: 1.0440 - val_output_3_1_mae: 0.7500 - val_output_3_2_mae: 1.2009
Epoch 19/20
9/9 [==============================] - 1s 92ms/step - loss: 3.1871 - output_1_1_loss: 1.0524 - output_1_2_loss: 1.2934 - output_2_1_loss: 0.8412 - output_1_1_mae: 0.8256 - output_1_2_mae: 0.9245 - output_2_1_mae: 0.7353 - output_2_2_mae: 0.7387 - output_3_1_mae: 0.7563 - output_3_2_mae: 0.7871 - val_loss: 3.6106 - val_output_1_1_loss: 0.8820 - val_output_1_2_loss: 1.9958 - val_output_2_1_loss: 0.7328 - val_output_1_1_mae: 0.7685 - val_output_1_2_mae: 1.1143 - val_output_2_1_mae: 0.6885 - val_output_2_2_mae: 1.0465 - val_output_3_1_mae: 0.7469 - val_output_3_2_mae: 1.1983
Epoch 20/20
9/9 [==============================] - 1s 87ms/step - loss: 3.1260 - output_1_1_loss: 1.0483 - output_1_2_loss: 1.2413 - output_2_1_loss: 0.8363 - output_1_1_mae: 0.8245 - output_1_2_mae: 0.8869 - output_2_1_mae: 0.7333 - output_2_2_mae: 0.7342 - output_3_1_mae: 0.7541 - output_3_2_mae: 0.8061 - val_loss: 3.9547 - val_output_1_1_loss: 0.8803 - val_output_1_2_loss: 2.3441 - val_output_2_1_loss: 0.7303 - val_output_1_1_mae: 0.7685 - val_output_1_2_mae: 1.2228 - val_output_2_1_mae: 0.6864 - val_output_2_2_mae: 1.0471 - val_output_3_1_mae: 0.7449 - val_output_3_2_mae: 1.1286





<keras.callbacks.History at 0x25a5f4cb4f0>importmatplotlib.pyplotaspltiterator=iter(w.train)x,y=iterator.get_next()input_width=80plt.plot(range(input_width,input_width+40),model.predict(x)[1][0],label='forecast')plt.plot(range(input_width,input_width+40),y[1][0],label='labels')plt.plot(range(input_width),x[0],label='inputs')plt.legend()<matplotlib.legend.Legend at 0x25a852bb2e0>