第17章 予測を使ってさらに予測を行う

In [2]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.losses import MeanSquaredError
from tensorflow.keras.metrics import MeanAbsoluteError
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, LSTM, Conv1D

import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.losses import MeanSquaredError from tensorflow.keras.metrics import MeanAbsoluteError from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.optimizers import Adam from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, LSTM, Conv1D

2023-11-23 10:28:40.029962: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-11-23 10:28:40.045119: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2023-11-23 10:28:40.159970: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-23 10:28:40.160215: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-23 10:28:40.187395: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-11-23 10:28:40.234423: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2023-11-23 10:28:40.235547: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-11-23 10:28:41.093645: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

In [3]:

url_train = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/train.csv'
url_val = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/val.csv'
url_test = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/test.csv'

df_train = pd.read_csv(url_train, index_col=0)
df_val = pd.read_csv(url_val, index_col=0)
df_test = pd.read_csv(url_test, index_col=0)

url_train = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/train.csv' url_val = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/val.csv' url_test = 'https://raw.githubusercontent.com/marcopeix/TimeSeriesForecastingInPython/master/data/test.csv' df_train = pd.read_csv(url_train, index_col=0) df_val = pd.read_csv(url_val, index_col=0) df_test = pd.read_csv(url_test, index_col=0)

In [4]:

class DataWindow:
    def __init__(self, input_width, label_width, shift, df_train, df_val, df_test, label_columns=None):
        # window size
        self.input_width = input_width
        self.label_width = label_width
        self.shift = shift
        self.total_window_size = input_width + shift
        
        # データ
        self.df_train = df_train
        self.df_val = df_val
        self.df_test = df_test
        
        # ラベル
        self.label_columns = label_columns
        if label_columns is not None:
            self.label_columns_indices = {name: i for i, name in enumerate(label_columns)}
        self.column_indices = {name: i for i, name in enumerate(self.df_train.columns)}
        
        # スライス
        self.input_slice = slice(0, input_width)
        self.input_indices = np.arange(self.total_window_size)[self.input_slice]
        
        # ラベル開始位置
        self.label_start = self.total_window_size - self.label_width
        self.labels_slice = slice(self.label_start, None)
        self.label_indices = np.arange(self.total_window_size)[self.labels_slice]
    
    def split_to_inputs_labels(self, features):
        inputs = features[:, self.input_slice, :]
        labels = features[:, self.labels_slice, :]
        if self.label_columns is not None:
            labels = tf.stack([labels[:, :, self.column_indices[name]] for name in self.label_columns], axis=-1)
        inputs.set_shape([None, self.input_width, None])
        labels.set_shape([None, self.label_width, None])
        return inputs, labels
    
    def plot(self, plot_col: str, model=None, max_subplots=3):
        inputs, labels = self.sample_batch
        plt.figure(figsize=(12, 8))
        plot_col_index = self.column_indices[plot_col]
        n_max = min(max_subplots, len(inputs))
        
        for n in range(n_max):
            plt.subplot(n_max, 1, n+1)
            plt.ylabel(f'{plot_col} [scaled]')
            plt.plot(self.input_indices, inputs[n, :, plot_col_index], label='Inputs', marker='.', zorder=-10)
            
            if self.label_columns:
                label_col_index = self.label_columns_indices.get(plot_col, None)
            else:
                label_col_index = plot_col_index
            
            if label_col_index is None:
                continue
            
            plt.scatter(self.label_indices, labels[n, :, label_col_index], edgecolors='k', label='Labels', c='tab:green', s=64)
            
            if model is not None:
                predictions = model(inputs)
                plt.scatter(self.label_indices, predictions[n, :, label_col_index], marker='X', edgecolors='k', label='Predictions', c='tab:red', s=64)
            
            if n == 0:
                plt.legend()
        plt.xlabel('Time (h)')
    
    def make_dataset(self, data):
        data = np.array(data, dtype=np.float32)
        ds = tf.keras.utils.timeseries_dataset_from_array(
            data=data,
            targets=None,
            sequence_length=self.total_window_size,
            sequence_stride=1,
            shuffle=True,
            batch_size=32,
        )
        ds = ds.map(self.split_to_inputs_labels)
        return ds
    
    @property
    def train(self):
        return self.make_dataset(self.df_train)
    
    @property
    def val(self):
        return self.make_dataset(self.df_val)
    
    @property
    def test(self):
        return self.make_dataset(self.df_test)
    
    @property
    def sample_batch(self):
        """Get and cache an example batch of `inputs, labels` for plotting."""
        result = getattr(self, '_sample_batch', None)
        if result is None:
            result = next(iter(self.train))
            self._sample_batch = result
        return result

class DataWindow: def __init__(self, input_width, label_width, shift, df_train, df_val, df_test, label_columns=None): # window size self.input_width = input_width self.label_width = label_width self.shift = shift self.total_window_size = input_width + shift # データ self.df_train = df_train self.df_val = df_val self.df_test = df_test # ラベル self.label_columns = label_columns if label_columns is not None: self.label_columns_indices = {name: i for i, name in enumerate(label_columns)} self.column_indices = {name: i for i, name in enumerate(self.df_train.columns)} # スライス self.input_slice = slice(0, input_width) self.input_indices = np.arange(self.total_window_size)[self.input_slice] # ラベル開始位置 self.label_start = self.total_window_size - self.label_width self.labels_slice = slice(self.label_start, None) self.label_indices = np.arange(self.total_window_size)[self.labels_slice] def split_to_inputs_labels(self, features): inputs = features[:, self.input_slice, :] labels = features[:, self.labels_slice, :] if self.label_columns is not None: labels = tf.stack([labels[:, :, self.column_indices[name]] for name in self.label_columns], axis=-1) inputs.set_shape([None, self.input_width, None]) labels.set_shape([None, self.label_width, None]) return inputs, labels def plot(self, plot_col: str, model=None, max_subplots=3): inputs, labels = self.sample_batch plt.figure(figsize=(12, 8)) plot_col_index = self.column_indices[plot_col] n_max = min(max_subplots, len(inputs)) for n in range(n_max): plt.subplot(n_max, 1, n+1) plt.ylabel(f'{plot_col} [scaled]') plt.plot(self.input_indices, inputs[n, :, plot_col_index], label='Inputs', marker='.', zorder=-10) if self.label_columns: label_col_index = self.label_columns_indices.get(plot_col, None) else: label_col_index = plot_col_index if label_col_index is None: continue plt.scatter(self.label_indices, labels[n, :, label_col_index], edgecolors='k', label='Labels', c='tab:green', s=64) if model is not None: predictions = model(inputs) plt.scatter(self.label_indices, predictions[n, :, label_col_index], marker='X', edgecolors='k', label='Predictions', c='tab:red', s=64) if n == 0: plt.legend() plt.xlabel('Time (h)') def make_dataset(self, data): data = np.array(data, dtype=np.float32) ds = tf.keras.utils.timeseries_dataset_from_array( data=data, targets=None, sequence_length=self.total_window_size, sequence_stride=1, shuffle=True, batch_size=32, ) ds = ds.map(self.split_to_inputs_labels) return ds @property def train(self): return self.make_dataset(self.df_train) @property def val(self): return self.make_dataset(self.df_val) @property def test(self): return self.make_dataset(self.df_test) @property def sample_batch(self): """Get and cache an example batch of `inputs, labels` for plotting.""" result = getattr(self, '_sample_batch', None) if result is None: result = next(iter(self.train)) self._sample_batch = result return result

In [5]:

# for training
def compile_and_fit(model, window, patience=3, max_epochs=50):
    early_stopping = EarlyStopping(
        monitor='val_loss',
        patience=patience,
        mode='min'
    )
    model.compile(
        loss=MeanSquaredError(),
        optimizer=Adam(),
        metrics=[MeanAbsoluteError()]
    )
    history = model.fit(
        window.train,
        epochs=max_epochs,
        validation_data=window.val,
        callbacks=[early_stopping]
    )
    return history

# for training def compile_and_fit(model, window, patience=3, max_epochs=50): early_stopping = EarlyStopping( monitor='val_loss', patience=patience, mode='min' ) model.compile( loss=MeanSquaredError(), optimizer=Adam(), metrics=[MeanAbsoluteError()] ) history = model.fit( window.train, epochs=max_epochs, validation_data=window.val, callbacks=[early_stopping] ) return history

In [6]:

# models
class Baseline(Model):
    def __init__(self, label_index=None):
        super().__init__()
        self.label_index = label_index
        
    def call(self, inputs):
        if self.label_index is None:
            return inputs
        elif isinstance(self.label_index, list):
            tensors = []
            for index in self.label_index:
                res = inputs[:, :, index]
                res = res[:, :, tf.newaxis]
                tensors.append(res)
            return tf.concat(tensors, axis=-1)
        else:
            res = inputs[:, :, self.label_index]
            return res[:, :, tf.newaxis]

class MultiStepLastBaseline(Model):
    def __init__(self, label_index=None):
        super().__init__()
        self.label_index = label_index
    
    def call(self, inputs):
        if self.label_index is None:
            return tf.tile(inputs[:, -1:, :], [1, 24, 1])
        return tf.tile(inputs[:, -1:, self.label_index:], [1, 24, 1])
    
class RepeatBaseline(Model):
    def __init__(self, label_index=None):
        super().__init__()
        self.label_index = label_index
    
    def call(self, inputs):
        return inputs[:, :, self.label_index:]

# 線形モデル
ms_linear = Sequential([Dense(units=1, kernel_initializer=tf.initializers.zeros)])
# DNN
ms_dense = Sequential([
    Dense(units=64, activation='relu'),
    Dense(units=64, activation='relu'),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])
# LSTM
ms_lstm_model = Sequential([
    LSTM(32, return_sequences=True),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])
# CNN
KERNEL_WIDTH = 3
ms_cnn_model = Sequential([
    Conv1D(filters=32, kernel_size=(KERNEL_WIDTH,), activation='relu'),
    Dense(units=32, activation='relu'),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])
ms_cnn_lstm_model = Sequential([
    Conv1D(filters=32, kernel_size=(KERNEL_WIDTH,), activation='relu'),
    LSTM(32, return_sequences=True),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])

# models class Baseline(Model): def __init__(self, label_index=None): super().__init__() self.label_index = label_index def call(self, inputs): if self.label_index is None: return inputs elif isinstance(self.label_index, list): tensors = [] for index in self.label_index: res = inputs[:, :, index] res = res[:, :, tf.newaxis] tensors.append(res) return tf.concat(tensors, axis=-1) else: res = inputs[:, :, self.label_index] return res[:, :, tf.newaxis] class MultiStepLastBaseline(Model): def __init__(self, label_index=None): super().__init__() self.label_index = label_index def call(self, inputs): if self.label_index is None: return tf.tile(inputs[:, -1:, :], [1, 24, 1]) return tf.tile(inputs[:, -1:, self.label_index:], [1, 24, 1]) class RepeatBaseline(Model): def __init__(self, label_index=None): super().__init__() self.label_index = label_index def call(self, inputs): return inputs[:, :, self.label_index:] # 線形モデル ms_linear = Sequential([Dense(units=1, kernel_initializer=tf.initializers.zeros)]) # DNN ms_dense = Sequential([ Dense(units=64, activation='relu'), Dense(units=64, activation='relu'), Dense(units=1, kernel_initializer=tf.initializers.zeros) ]) # LSTM ms_lstm_model = Sequential([ LSTM(32, return_sequences=True), Dense(units=1, kernel_initializer=tf.initializers.zeros) ]) # CNN KERNEL_WIDTH = 3 ms_cnn_model = Sequential([ Conv1D(filters=32, kernel_size=(KERNEL_WIDTH,), activation='relu'), Dense(units=32, activation='relu'), Dense(units=1, kernel_initializer=tf.initializers.zeros) ]) ms_cnn_lstm_model = Sequential([ Conv1D(filters=32, kernel_size=(KERNEL_WIDTH,), activation='relu'), LSTM(32, return_sequences=True), Dense(units=1, kernel_initializer=tf.initializers.zeros) ])

In [7]:

# マルチステップ
column_indices = {name: i for i, name in enumerate(df_train.columns)}
multi_window = DataWindow(input_width=24, label_width=24, shift=24, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume'])
LABEL_WIDTH = 24
INPUT_WIDTH = LABEL_WIDTH + KERNEL_WIDTH - 1
multi_conv_window = DataWindow(input_width=INPUT_WIDTH, label_width=LABEL_WIDTH, shift=24, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume'])
ms_baseline_last = MultiStepLastBaseline(label_index=column_indices['traffic_volume'])
ms_baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()])
ms_baseline_repeat = RepeatBaseline(label_index=column_indices['traffic_volume'])
ms_baseline_repeat.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()])

ms_val_performance = {}
ms_test_performance = {}

ms_val_performance['Baseline - Last'] = ms_baseline_last.evaluate(multi_window.val)
ms_test_performance['Baseline - Last'] = ms_baseline_last.evaluate(multi_window.test)
ms_val_performance['Baseline - Repeat'] = ms_baseline_repeat.evaluate(multi_window.val)
ms_test_performance['Baseline - Repeat'] = ms_baseline_repeat.evaluate(multi_window.test)
history = compile_and_fit(ms_linear, multi_window)
ms_val_performance['Linear'] = ms_linear.evaluate(multi_window.val)
ms_test_performance['Linear'] = ms_linear.evaluate(multi_window.test)
history = compile_and_fit(ms_dense, multi_window)
ms_val_performance['Dense'] = ms_dense.evaluate(multi_window.val)
ms_test_performance['Dense'] = ms_dense.evaluate(multi_window.test)
history = compile_and_fit(ms_lstm_model, multi_window)
ms_val_performance['LSTM'] = ms_lstm_model.evaluate(multi_window.val)
ms_test_performance['LSTM'] = ms_lstm_model.evaluate(multi_window.test)
history = compile_and_fit(ms_cnn_model, multi_conv_window)
ms_val_performance['CNN'] = ms_cnn_model.evaluate(multi_conv_window.val)
ms_test_performance['CNN'] = ms_cnn_model.evaluate(multi_conv_window.test)
history = compile_and_fit(ms_cnn_lstm_model, multi_conv_window)
ms_val_performance['CNN + LSTM'] = ms_cnn_lstm_model.evaluate(multi_conv_window.val)
ms_test_performance['CNN + LSTM'] = ms_cnn_lstm_model.evaluate(multi_conv_window.test)

# マルチステップ column_indices = {name: i for i, name in enumerate(df_train.columns)} multi_window = DataWindow(input_width=24, label_width=24, shift=24, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume']) LABEL_WIDTH = 24 INPUT_WIDTH = LABEL_WIDTH + KERNEL_WIDTH - 1 multi_conv_window = DataWindow(input_width=INPUT_WIDTH, label_width=LABEL_WIDTH, shift=24, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume']) ms_baseline_last = MultiStepLastBaseline(label_index=column_indices['traffic_volume']) ms_baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()]) ms_baseline_repeat = RepeatBaseline(label_index=column_indices['traffic_volume']) ms_baseline_repeat.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()]) ms_val_performance = {} ms_test_performance = {} ms_val_performance['Baseline - Last'] = ms_baseline_last.evaluate(multi_window.val) ms_test_performance['Baseline - Last'] = ms_baseline_last.evaluate(multi_window.test) ms_val_performance['Baseline - Repeat'] = ms_baseline_repeat.evaluate(multi_window.val) ms_test_performance['Baseline - Repeat'] = ms_baseline_repeat.evaluate(multi_window.test) history = compile_and_fit(ms_linear, multi_window) ms_val_performance['Linear'] = ms_linear.evaluate(multi_window.val) ms_test_performance['Linear'] = ms_linear.evaluate(multi_window.test) history = compile_and_fit(ms_dense, multi_window) ms_val_performance['Dense'] = ms_dense.evaluate(multi_window.val) ms_test_performance['Dense'] = ms_dense.evaluate(multi_window.test) history = compile_and_fit(ms_lstm_model, multi_window) ms_val_performance['LSTM'] = ms_lstm_model.evaluate(multi_window.val) ms_test_performance['LSTM'] = ms_lstm_model.evaluate(multi_window.test) history = compile_and_fit(ms_cnn_model, multi_conv_window) ms_val_performance['CNN'] = ms_cnn_model.evaluate(multi_conv_window.val) ms_test_performance['CNN'] = ms_cnn_model.evaluate(multi_conv_window.test) history = compile_and_fit(ms_cnn_lstm_model, multi_conv_window) ms_val_performance['CNN + LSTM'] = ms_cnn_lstm_model.evaluate(multi_conv_window.val) ms_test_performance['CNN + LSTM'] = ms_cnn_lstm_model.evaluate(multi_conv_window.test)

109/109 [==============================] - 0s 3ms/step - loss: 0.1875 - mean_absolute_error: 0.3522
54/54 [==============================] - 0s 3ms/step - loss: 0.1814 - mean_absolute_error: 0.3473
109/109 [==============================] - 0s 2ms/step - loss: 0.2065 - mean_absolute_error: 0.3473
54/54 [==============================] - 0s 2ms/step - loss: 0.2018 - mean_absolute_error: 0.3413
Epoch 1/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0897 - mean_absolute_error: 0.2464 - val_loss: 0.0457 - val_mean_absolute_error: 0.1821
Epoch 2/50
383/383 [==============================] - 1s 4ms/step - loss: 0.0297 - mean_absolute_error: 0.1365 - val_loss: 0.0262 - val_mean_absolute_error: 0.1259
Epoch 3/50
383/383 [==============================] - 1s 2ms/step - loss: 0.0200 - mean_absolute_error: 0.1043 - val_loss: 0.0213 - val_mean_absolute_error: 0.1083
Epoch 4/50
383/383 [==============================] - 1s 2ms/step - loss: 0.0176 - mean_absolute_error: 0.0954 - val_loss: 0.0197 - val_mean_absolute_error: 0.1007
Epoch 5/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0168 - mean_absolute_error: 0.0914 - val_loss: 0.0189 - val_mean_absolute_error: 0.0961
Epoch 6/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0164 - mean_absolute_error: 0.0887 - val_loss: 0.0185 - val_mean_absolute_error: 0.0932
Epoch 7/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0162 - mean_absolute_error: 0.0868 - val_loss: 0.0183 - val_mean_absolute_error: 0.0910
Epoch 8/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0161 - mean_absolute_error: 0.0857 - val_loss: 0.0182 - val_mean_absolute_error: 0.0898
Epoch 9/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0161 - mean_absolute_error: 0.0850 - val_loss: 0.0182 - val_mean_absolute_error: 0.0889
Epoch 10/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0161 - mean_absolute_error: 0.0846 - val_loss: 0.0182 - val_mean_absolute_error: 0.0889
Epoch 11/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0161 - mean_absolute_error: 0.0843 - val_loss: 0.0182 - val_mean_absolute_error: 0.0887
Epoch 12/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0161 - mean_absolute_error: 0.0841 - val_loss: 0.0182 - val_mean_absolute_error: 0.0886
109/109 [==============================] - 0s 2ms/step - loss: 0.0182 - mean_absolute_error: 0.0886
54/54 [==============================] - 0s 3ms/step - loss: 0.0142 - mean_absolute_error: 0.0768
Epoch 1/50
383/383 [==============================] - 3s 6ms/step - loss: 0.0298 - mean_absolute_error: 0.1096 - val_loss: 0.0172 - val_mean_absolute_error: 0.0849
Epoch 2/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0148 - mean_absolute_error: 0.0794 - val_loss: 0.0166 - val_mean_absolute_error: 0.0850
Epoch 3/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0143 - mean_absolute_error: 0.0788 - val_loss: 0.0160 - val_mean_absolute_error: 0.0842
Epoch 4/50
383/383 [==============================] - 3s 6ms/step - loss: 0.0137 - mean_absolute_error: 0.0780 - val_loss: 0.0158 - val_mean_absolute_error: 0.0868
Epoch 5/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0134 - mean_absolute_error: 0.0774 - val_loss: 0.0154 - val_mean_absolute_error: 0.0821
Epoch 6/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0133 - mean_absolute_error: 0.0771 - val_loss: 0.0150 - val_mean_absolute_error: 0.0822
Epoch 7/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0131 - mean_absolute_error: 0.0766 - val_loss: 0.0148 - val_mean_absolute_error: 0.0806
Epoch 8/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0131 - mean_absolute_error: 0.0762 - val_loss: 0.0148 - val_mean_absolute_error: 0.0801
Epoch 9/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0130 - mean_absolute_error: 0.0761 - val_loss: 0.0160 - val_mean_absolute_error: 0.0834
Epoch 10/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0129 - mean_absolute_error: 0.0757 - val_loss: 0.0146 - val_mean_absolute_error: 0.0799
Epoch 11/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0129 - mean_absolute_error: 0.0755 - val_loss: 0.0149 - val_mean_absolute_error: 0.0817
Epoch 12/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0129 - mean_absolute_error: 0.0756 - val_loss: 0.0145 - val_mean_absolute_error: 0.0801
Epoch 13/50
383/383 [==============================] - 3s 9ms/step - loss: 0.0128 - mean_absolute_error: 0.0753 - val_loss: 0.0146 - val_mean_absolute_error: 0.0821
Epoch 14/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0128 - mean_absolute_error: 0.0752 - val_loss: 0.0145 - val_mean_absolute_error: 0.0790
Epoch 15/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0127 - mean_absolute_error: 0.0750 - val_loss: 0.0144 - val_mean_absolute_error: 0.0805
Epoch 16/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0127 - mean_absolute_error: 0.0749 - val_loss: 0.0143 - val_mean_absolute_error: 0.0787
Epoch 17/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0126 - mean_absolute_error: 0.0746 - val_loss: 0.0145 - val_mean_absolute_error: 0.0805
Epoch 18/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0126 - mean_absolute_error: 0.0745 - val_loss: 0.0143 - val_mean_absolute_error: 0.0781
Epoch 19/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0126 - mean_absolute_error: 0.0745 - val_loss: 0.0145 - val_mean_absolute_error: 0.0796
109/109 [==============================] - 1s 4ms/step - loss: 0.0145 - mean_absolute_error: 0.0796
54/54 [==============================] - 0s 4ms/step - loss: 0.0100 - mean_absolute_error: 0.0657
Epoch 1/50
383/383 [==============================] - 11s 24ms/step - loss: 0.0477 - mean_absolute_error: 0.1570 - val_loss: 0.0209 - val_mean_absolute_error: 0.1023
Epoch 2/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0167 - mean_absolute_error: 0.0899 - val_loss: 0.0175 - val_mean_absolute_error: 0.0906
Epoch 3/50
383/383 [==============================] - 9s 24ms/step - loss: 0.0150 - mean_absolute_error: 0.0830 - val_loss: 0.0165 - val_mean_absolute_error: 0.0869
Epoch 4/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0143 - mean_absolute_error: 0.0803 - val_loss: 0.0159 - val_mean_absolute_error: 0.0836
Epoch 5/50
383/383 [==============================] - 9s 24ms/step - loss: 0.0139 - mean_absolute_error: 0.0789 - val_loss: 0.0159 - val_mean_absolute_error: 0.0852
Epoch 6/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0137 - mean_absolute_error: 0.0777 - val_loss: 0.0153 - val_mean_absolute_error: 0.0828
Epoch 7/50
383/383 [==============================] - 9s 24ms/step - loss: 0.0134 - mean_absolute_error: 0.0766 - val_loss: 0.0153 - val_mean_absolute_error: 0.0820
Epoch 8/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0133 - mean_absolute_error: 0.0764 - val_loss: 0.0151 - val_mean_absolute_error: 0.0826
Epoch 9/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0131 - mean_absolute_error: 0.0756 - val_loss: 0.0148 - val_mean_absolute_error: 0.0783
Epoch 10/50
383/383 [==============================] - 9s 24ms/step - loss: 0.0130 - mean_absolute_error: 0.0752 - val_loss: 0.0147 - val_mean_absolute_error: 0.0803
Epoch 11/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0129 - mean_absolute_error: 0.0747 - val_loss: 0.0156 - val_mean_absolute_error: 0.0827
Epoch 12/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0129 - mean_absolute_error: 0.0745 - val_loss: 0.0150 - val_mean_absolute_error: 0.0803
Epoch 13/50
383/383 [==============================] - 9s 24ms/step - loss: 0.0127 - mean_absolute_error: 0.0740 - val_loss: 0.0149 - val_mean_absolute_error: 0.0799
109/109 [==============================] - 1s 11ms/step - loss: 0.0149 - mean_absolute_error: 0.0799
54/54 [==============================] - 1s 9ms/step - loss: 0.0109 - mean_absolute_error: 0.0663
Epoch 1/50
383/383 [==============================] - 4s 8ms/step - loss: 0.0354 - mean_absolute_error: 0.1243 - val_loss: 0.0169 - val_mean_absolute_error: 0.0852
Epoch 2/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0144 - mean_absolute_error: 0.0788 - val_loss: 0.0164 - val_mean_absolute_error: 0.0867
Epoch 3/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0140 - mean_absolute_error: 0.0781 - val_loss: 0.0160 - val_mean_absolute_error: 0.0839
Epoch 4/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0138 - mean_absolute_error: 0.0778 - val_loss: 0.0160 - val_mean_absolute_error: 0.0862
Epoch 5/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0137 - mean_absolute_error: 0.0775 - val_loss: 0.0159 - val_mean_absolute_error: 0.0863
Epoch 6/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0135 - mean_absolute_error: 0.0772 - val_loss: 0.0156 - val_mean_absolute_error: 0.0818
Epoch 7/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0133 - mean_absolute_error: 0.0766 - val_loss: 0.0154 - val_mean_absolute_error: 0.0844
Epoch 8/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0132 - mean_absolute_error: 0.0761 - val_loss: 0.0151 - val_mean_absolute_error: 0.0835
Epoch 9/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0131 - mean_absolute_error: 0.0758 - val_loss: 0.0150 - val_mean_absolute_error: 0.0823
Epoch 10/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0129 - mean_absolute_error: 0.0751 - val_loss: 0.0147 - val_mean_absolute_error: 0.0800
Epoch 11/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0128 - mean_absolute_error: 0.0746 - val_loss: 0.0148 - val_mean_absolute_error: 0.0794
Epoch 12/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0127 - mean_absolute_error: 0.0743 - val_loss: 0.0146 - val_mean_absolute_error: 0.0793
Epoch 13/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0126 - mean_absolute_error: 0.0741 - val_loss: 0.0145 - val_mean_absolute_error: 0.0806
Epoch 14/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0126 - mean_absolute_error: 0.0741 - val_loss: 0.0144 - val_mean_absolute_error: 0.0797
Epoch 15/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0125 - mean_absolute_error: 0.0735 - val_loss: 0.0144 - val_mean_absolute_error: 0.0795
Epoch 16/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0125 - mean_absolute_error: 0.0737 - val_loss: 0.0146 - val_mean_absolute_error: 0.0824
Epoch 17/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0125 - mean_absolute_error: 0.0735 - val_loss: 0.0143 - val_mean_absolute_error: 0.0796
Epoch 18/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0124 - mean_absolute_error: 0.0731 - val_loss: 0.0143 - val_mean_absolute_error: 0.0795
Epoch 19/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0123 - mean_absolute_error: 0.0729 - val_loss: 0.0146 - val_mean_absolute_error: 0.0774
Epoch 20/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0123 - mean_absolute_error: 0.0729 - val_loss: 0.0144 - val_mean_absolute_error: 0.0807
109/109 [==============================] - 1s 4ms/step - loss: 0.0144 - mean_absolute_error: 0.0807
54/54 [==============================] - 0s 4ms/step - loss: 0.0098 - mean_absolute_error: 0.0655
Epoch 1/50
383/383 [==============================] - 13s 29ms/step - loss: 0.0420 - mean_absolute_error: 0.1441 - val_loss: 0.0171 - val_mean_absolute_error: 0.0897
Epoch 2/50
383/383 [==============================] - 11s 27ms/step - loss: 0.0146 - mean_absolute_error: 0.0824 - val_loss: 0.0163 - val_mean_absolute_error: 0.0865
Epoch 3/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0141 - mean_absolute_error: 0.0798 - val_loss: 0.0161 - val_mean_absolute_error: 0.0855
Epoch 4/50
383/383 [==============================] - 13s 33ms/step - loss: 0.0139 - mean_absolute_error: 0.0787 - val_loss: 0.0158 - val_mean_absolute_error: 0.0849
Epoch 5/50
383/383 [==============================] - 15s 38ms/step - loss: 0.0138 - mean_absolute_error: 0.0782 - val_loss: 0.0156 - val_mean_absolute_error: 0.0832
Epoch 6/50
383/383 [==============================] - 17s 45ms/step - loss: 0.0136 - mean_absolute_error: 0.0774 - val_loss: 0.0155 - val_mean_absolute_error: 0.0811
Epoch 7/50
383/383 [==============================] - 13s 34ms/step - loss: 0.0134 - mean_absolute_error: 0.0767 - val_loss: 0.0156 - val_mean_absolute_error: 0.0859
Epoch 8/50
383/383 [==============================] - 15s 40ms/step - loss: 0.0132 - mean_absolute_error: 0.0759 - val_loss: 0.0159 - val_mean_absolute_error: 0.0884
Epoch 9/50
383/383 [==============================] - 12s 30ms/step - loss: 0.0129 - mean_absolute_error: 0.0752 - val_loss: 0.0147 - val_mean_absolute_error: 0.0786
Epoch 10/50
383/383 [==============================] - 14s 37ms/step - loss: 0.0127 - mean_absolute_error: 0.0747 - val_loss: 0.0151 - val_mean_absolute_error: 0.0859
Epoch 11/50
383/383 [==============================] - 12s 30ms/step - loss: 0.0126 - mean_absolute_error: 0.0743 - val_loss: 0.0142 - val_mean_absolute_error: 0.0807
Epoch 12/50
383/383 [==============================] - 13s 33ms/step - loss: 0.0125 - mean_absolute_error: 0.0737 - val_loss: 0.0149 - val_mean_absolute_error: 0.0841
Epoch 13/50
383/383 [==============================] - 13s 34ms/step - loss: 0.0123 - mean_absolute_error: 0.0731 - val_loss: 0.0138 - val_mean_absolute_error: 0.0778
Epoch 14/50
383/383 [==============================] - 14s 36ms/step - loss: 0.0119 - mean_absolute_error: 0.0718 - val_loss: 0.0135 - val_mean_absolute_error: 0.0744
Epoch 15/50
383/383 [==============================] - 14s 36ms/step - loss: 0.0118 - mean_absolute_error: 0.0712 - val_loss: 0.0130 - val_mean_absolute_error: 0.0747
Epoch 16/50
383/383 [==============================] - 11s 30ms/step - loss: 0.0116 - mean_absolute_error: 0.0704 - val_loss: 0.0129 - val_mean_absolute_error: 0.0752
Epoch 17/50
383/383 [==============================] - 13s 33ms/step - loss: 0.0115 - mean_absolute_error: 0.0700 - val_loss: 0.0127 - val_mean_absolute_error: 0.0722
Epoch 18/50
383/383 [==============================] - 11s 29ms/step - loss: 0.0113 - mean_absolute_error: 0.0692 - val_loss: 0.0124 - val_mean_absolute_error: 0.0727
Epoch 19/50
383/383 [==============================] - 11s 29ms/step - loss: 0.0112 - mean_absolute_error: 0.0686 - val_loss: 0.0123 - val_mean_absolute_error: 0.0728
Epoch 20/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0110 - mean_absolute_error: 0.0678 - val_loss: 0.0123 - val_mean_absolute_error: 0.0721
Epoch 21/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0109 - mean_absolute_error: 0.0674 - val_loss: 0.0119 - val_mean_absolute_error: 0.0690
Epoch 22/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0108 - mean_absolute_error: 0.0668 - val_loss: 0.0119 - val_mean_absolute_error: 0.0696
Epoch 23/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0107 - mean_absolute_error: 0.0665 - val_loss: 0.0126 - val_mean_absolute_error: 0.0766
Epoch 24/50
383/383 [==============================] - 10s 26ms/step - loss: 0.0106 - mean_absolute_error: 0.0663 - val_loss: 0.0119 - val_mean_absolute_error: 0.0730
Epoch 25/50
383/383 [==============================] - 11s 27ms/step - loss: 0.0105 - mean_absolute_error: 0.0657 - val_loss: 0.0120 - val_mean_absolute_error: 0.0693
Epoch 26/50
383/383 [==============================] - 11s 29ms/step - loss: 0.0104 - mean_absolute_error: 0.0655 - val_loss: 0.0116 - val_mean_absolute_error: 0.0687
Epoch 27/50
383/383 [==============================] - 16s 42ms/step - loss: 0.0103 - mean_absolute_error: 0.0650 - val_loss: 0.0123 - val_mean_absolute_error: 0.0735
Epoch 28/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0102 - mean_absolute_error: 0.0644 - val_loss: 0.0117 - val_mean_absolute_error: 0.0703
Epoch 29/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0102 - mean_absolute_error: 0.0643 - val_loss: 0.0118 - val_mean_absolute_error: 0.0671
109/109 [==============================] - 1s 11ms/step - loss: 0.0118 - mean_absolute_error: 0.0671
54/54 [==============================] - 1s 10ms/step - loss: 0.0081 - mean_absolute_error: 0.0531

In [8]:

# multi stepの結果比較
plt.title('Multi Step')
plt.xlabel('Models')
plt.ylabel('MAE')
plt.bar(ms_val_performance.keys(), [v[1] for v in ms_val_performance.values()], width=-0.25, align='edge', label='Validation')
plt.bar(ms_test_performance.keys(), [v[1] for v in ms_test_performance.values()], width=0.25, align='edge', label='Test', hatch='/')
plt.legend()
plt.xticks(rotation=45, ha='right')
plt.tight_layout()

# multi stepの結果比較 plt.title('Multi Step') plt.xlabel('Models') plt.ylabel('MAE') plt.bar(ms_val_performance.keys(), [v[1] for v in ms_val_performance.values()], width=-0.25, align='edge', label='Validation') plt.bar(ms_test_performance.keys(), [v[1] for v in ms_test_performance.values()], width=0.25, align='edge', label='Test', hatch='/') plt.legend() plt.xticks(rotation=45, ha='right') plt.tight_layout()

ARLSTMアーキテクチャを調べる¶

ARLSTMモデルを構築する¶

In [ ]: