第15章 LSTMで過去を記憶する

In [9]:

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

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

In [2]:

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 [3]:

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 [7]:

# 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
linear = Sequential([Dense(units=1)])
ms_linear = Sequential([Dense(units=1, kernel_initializer=tf.initializers.zeros)])
mo_linear = Sequential([Dense(units=2)])
# DNN
dense = Sequential([
    Dense(units=64, activation='relu'),
    Dense(units=64, activation='relu'),
    Dense(units=1)
])
ms_dense = Sequential([
    Dense(units=64, activation='relu'),
    Dense(units=64, activation='relu'),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])
mo_dense = Sequential([
    Dense(units=64, activation='relu'),
    Dense(units=64, activation='relu'),
    Dense(units=2)
])

# 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 linear = Sequential([Dense(units=1)]) ms_linear = Sequential([Dense(units=1, kernel_initializer=tf.initializers.zeros)]) mo_linear = Sequential([Dense(units=2)]) # DNN dense = Sequential([ Dense(units=64, activation='relu'), Dense(units=64, activation='relu'), Dense(units=1) ]) ms_dense = Sequential([ Dense(units=64, activation='relu'), Dense(units=64, activation='relu'), Dense(units=1, kernel_initializer=tf.initializers.zeros) ]) mo_dense = Sequential([ Dense(units=64, activation='relu'), Dense(units=64, activation='relu'), Dense(units=2) ])

In [8]:

# シングルステップ
single_step_window = DataWindow(input_width=1, label_width=1, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume'])
wide_window = DataWindow(input_width=24, label_width=24, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume'])
column_indices = {name: i for i, name in enumerate(df_train.columns)}
baseline_last = Baseline(column_indices['traffic_volume'])
baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()])

val_performance = {}
test_performance = {}

## ベースライン
val_performance['Baseline - Last'] = baseline_last.evaluate(single_step_window.val)
test_performance['Baseline - Last'] = baseline_last.evaluate(single_step_window.test, verbose=0)
## 線形モデル
history = compile_and_fit(linear, single_step_window)
val_performance['Linear'] = linear.evaluate(single_step_window.val)
test_performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0)
## DNN
history = compile_and_fit(dense, single_step_window)
val_performance['Dense'] = dense.evaluate(single_step_window.val)
test_performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)

# マルチステップ
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'])
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 = {}

## ベースライン
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)
## 線形モデル
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)
## DNN
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)

# 多変数アウトプット
col_names = ['temp', 'traffic_volume']
mo_single_step_window = DataWindow(input_width=1, label_width=1, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=col_names)
mo_wide_window = DataWindow(input_width=24, label_width=24, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=col_names)
mo_baseline_last = Baseline(label_index=[column_indices[col] for col in col_names])
mo_baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()])

mo_val_performance = {}
mo_test_performance = {}

## ベースライン
mo_val_performance['Baseline - Last'] = mo_baseline_last.evaluate(mo_single_step_window.val)
mo_test_performance['Baseline - Last'] = mo_baseline_last.evaluate(mo_single_step_window.val)
## 線形モデル
history = compile_and_fit(mo_linear, mo_single_step_window)
mo_val_performance['Linear'] = mo_linear.evaluate(mo_single_step_window.val)
mo_test_performance['Linear'] = mo_linear.evaluate(mo_single_step_window.test)
## DNN
history = compile_and_fit(mo_dense, mo_single_step_window)
mo_val_performance['Dense'] = mo_dense.evaluate(mo_single_step_window.val)
mo_test_performance['Dense'] = mo_dense.evaluate(mo_single_step_window.test)

# シングルステップ single_step_window = DataWindow(input_width=1, label_width=1, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume']) wide_window = DataWindow(input_width=24, label_width=24, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=['traffic_volume']) column_indices = {name: i for i, name in enumerate(df_train.columns)} baseline_last = Baseline(column_indices['traffic_volume']) baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()]) val_performance = {} test_performance = {} ## ベースライン val_performance['Baseline - Last'] = baseline_last.evaluate(single_step_window.val) test_performance['Baseline - Last'] = baseline_last.evaluate(single_step_window.test, verbose=0) ## 線形モデル history = compile_and_fit(linear, single_step_window) val_performance['Linear'] = linear.evaluate(single_step_window.val) test_performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0) ## DNN history = compile_and_fit(dense, single_step_window) val_performance['Dense'] = dense.evaluate(single_step_window.val) test_performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0) # マルチステップ 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']) 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 = {} ## ベースライン 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) ## 線形モデル 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) ## DNN 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) # 多変数アウトプット col_names = ['temp', 'traffic_volume'] mo_single_step_window = DataWindow(input_width=1, label_width=1, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=col_names) mo_wide_window = DataWindow(input_width=24, label_width=24, shift=1, df_train=df_train, df_val=df_val, df_test=df_test, label_columns=col_names) mo_baseline_last = Baseline(label_index=[column_indices[col] for col in col_names]) mo_baseline_last.compile(loss=MeanSquaredError(), metrics=[MeanAbsoluteError()]) mo_val_performance = {} mo_test_performance = {} ## ベースライン mo_val_performance['Baseline - Last'] = mo_baseline_last.evaluate(mo_single_step_window.val) mo_test_performance['Baseline - Last'] = mo_baseline_last.evaluate(mo_single_step_window.val) ## 線形モデル history = compile_and_fit(mo_linear, mo_single_step_window) mo_val_performance['Linear'] = mo_linear.evaluate(mo_single_step_window.val) mo_test_performance['Linear'] = mo_linear.evaluate(mo_single_step_window.test) ## DNN history = compile_and_fit(mo_dense, mo_single_step_window) mo_val_performance['Dense'] = mo_dense.evaluate(mo_single_step_window.val) mo_test_performance['Dense'] = mo_dense.evaluate(mo_single_step_window.test)

110/110 [==============================] - 0s 2ms/step - loss: 0.0133 - mean_absolute_error: 0.0831
Epoch 1/50
384/384 [==============================] - 2s 3ms/step - loss: 0.1478 - mean_absolute_error: 0.3296 - val_loss: 0.0949 - val_mean_absolute_error: 0.2637
Epoch 2/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0575 - mean_absolute_error: 0.1931 - val_loss: 0.0423 - val_mean_absolute_error: 0.1688
Epoch 3/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0287 - mean_absolute_error: 0.1313 - val_loss: 0.0253 - val_mean_absolute_error: 0.1276
Epoch 4/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0192 - mean_absolute_error: 0.1063 - val_loss: 0.0182 - val_mean_absolute_error: 0.1055
Epoch 5/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0150 - mean_absolute_error: 0.0927 - val_loss: 0.0146 - val_mean_absolute_error: 0.0928
Epoch 6/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0128 - mean_absolute_error: 0.0843 - val_loss: 0.0124 - val_mean_absolute_error: 0.0836
Epoch 7/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0115 - mean_absolute_error: 0.0791 - val_loss: 0.0112 - val_mean_absolute_error: 0.0770
Epoch 8/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0107 - mean_absolute_error: 0.0756 - val_loss: 0.0104 - val_mean_absolute_error: 0.0726
Epoch 9/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0102 - mean_absolute_error: 0.0732 - val_loss: 0.0099 - val_mean_absolute_error: 0.0712
Epoch 10/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0098 - mean_absolute_error: 0.0715 - val_loss: 0.0096 - val_mean_absolute_error: 0.0685
Epoch 11/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0096 - mean_absolute_error: 0.0702 - val_loss: 0.0094 - val_mean_absolute_error: 0.0679
Epoch 12/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0095 - mean_absolute_error: 0.0694 - val_loss: 0.0093 - val_mean_absolute_error: 0.0674
Epoch 13/50
384/384 [==============================] - 4s 9ms/step - loss: 0.0094 - mean_absolute_error: 0.0688 - val_loss: 0.0092 - val_mean_absolute_error: 0.0666
Epoch 14/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0093 - mean_absolute_error: 0.0685 - val_loss: 0.0091 - val_mean_absolute_error: 0.0678
Epoch 15/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0093 - mean_absolute_error: 0.0682 - val_loss: 0.0091 - val_mean_absolute_error: 0.0679
Epoch 16/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0680 - val_loss: 0.0091 - val_mean_absolute_error: 0.0686
Epoch 17/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0680 - val_loss: 0.0091 - val_mean_absolute_error: 0.0665
Epoch 18/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0093 - mean_absolute_error: 0.0678 - val_loss: 0.0091 - val_mean_absolute_error: 0.0666
Epoch 19/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0091 - val_mean_absolute_error: 0.0677
Epoch 20/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0090 - val_mean_absolute_error: 0.0665
Epoch 21/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0091 - val_mean_absolute_error: 0.0673
Epoch 22/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0090 - val_mean_absolute_error: 0.0663
Epoch 23/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0090 - val_mean_absolute_error: 0.0664
Epoch 24/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0678 - val_loss: 0.0090 - val_mean_absolute_error: 0.0663
Epoch 25/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0093 - mean_absolute_error: 0.0677 - val_loss: 0.0091 - val_mean_absolute_error: 0.0675
110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - mean_absolute_error: 0.0675
Epoch 1/50
384/384 [==============================] - 3s 5ms/step - loss: 0.0121 - mean_absolute_error: 0.0733 - val_loss: 0.0050 - val_mean_absolute_error: 0.0526
Epoch 2/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0048 - mean_absolute_error: 0.0510 - val_loss: 0.0043 - val_mean_absolute_error: 0.0504
Epoch 3/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0041 - mean_absolute_error: 0.0470 - val_loss: 0.0035 - val_mean_absolute_error: 0.0439
Epoch 4/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0038 - mean_absolute_error: 0.0449 - val_loss: 0.0031 - val_mean_absolute_error: 0.0416
Epoch 5/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0035 - mean_absolute_error: 0.0430 - val_loss: 0.0037 - val_mean_absolute_error: 0.0484
Epoch 6/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0034 - mean_absolute_error: 0.0425 - val_loss: 0.0027 - val_mean_absolute_error: 0.0385
Epoch 7/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0032 - mean_absolute_error: 0.0406 - val_loss: 0.0026 - val_mean_absolute_error: 0.0373
Epoch 8/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0032 - mean_absolute_error: 0.0409 - val_loss: 0.0026 - val_mean_absolute_error: 0.0372
Epoch 9/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0029 - mean_absolute_error: 0.0385 - val_loss: 0.0035 - val_mean_absolute_error: 0.0438
Epoch 10/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0029 - mean_absolute_error: 0.0386 - val_loss: 0.0023 - val_mean_absolute_error: 0.0351
Epoch 11/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0028 - mean_absolute_error: 0.0379 - val_loss: 0.0026 - val_mean_absolute_error: 0.0374
Epoch 12/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0029 - mean_absolute_error: 0.0381 - val_loss: 0.0022 - val_mean_absolute_error: 0.0342
Epoch 13/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0027 - mean_absolute_error: 0.0372 - val_loss: 0.0023 - val_mean_absolute_error: 0.0346
Epoch 14/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0027 - mean_absolute_error: 0.0366 - val_loss: 0.0022 - val_mean_absolute_error: 0.0343
Epoch 15/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0026 - mean_absolute_error: 0.0359 - val_loss: 0.0021 - val_mean_absolute_error: 0.0328
Epoch 16/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0026 - mean_absolute_error: 0.0354 - val_loss: 0.0022 - val_mean_absolute_error: 0.0342
Epoch 17/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0026 - mean_absolute_error: 0.0357 - val_loss: 0.0020 - val_mean_absolute_error: 0.0323
Epoch 18/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0026 - mean_absolute_error: 0.0358 - val_loss: 0.0021 - val_mean_absolute_error: 0.0337
Epoch 19/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0025 - mean_absolute_error: 0.0351 - val_loss: 0.0019 - val_mean_absolute_error: 0.0316
Epoch 20/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0025 - mean_absolute_error: 0.0348 - val_loss: 0.0022 - val_mean_absolute_error: 0.0356
Epoch 21/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0024 - mean_absolute_error: 0.0346 - val_loss: 0.0023 - val_mean_absolute_error: 0.0354
Epoch 22/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0024 - mean_absolute_error: 0.0345 - val_loss: 0.0020 - val_mean_absolute_error: 0.0327
110/110 [==============================] - 0s 3ms/step - loss: 0.0020 - mean_absolute_error: 0.0327
109/109 [==============================] - 0s 2ms/step - loss: 0.1875 - mean_absolute_error: 0.3522
54/54 [==============================] - 0s 2ms/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 [==============================] - 2s 4ms/step - loss: 0.0901 - mean_absolute_error: 0.2467 - val_loss: 0.0459 - val_mean_absolute_error: 0.1824
Epoch 2/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0299 - mean_absolute_error: 0.1371 - val_loss: 0.0261 - val_mean_absolute_error: 0.1253
Epoch 3/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0201 - mean_absolute_error: 0.1045 - val_loss: 0.0213 - val_mean_absolute_error: 0.1074
Epoch 4/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0177 - mean_absolute_error: 0.0955 - val_loss: 0.0196 - val_mean_absolute_error: 0.1000
Epoch 5/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0168 - mean_absolute_error: 0.0914 - val_loss: 0.0189 - val_mean_absolute_error: 0.0957
Epoch 6/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0164 - mean_absolute_error: 0.0887 - val_loss: 0.0185 - val_mean_absolute_error: 0.0926
Epoch 7/50
383/383 [==============================] - 1s 3ms/step - loss: 0.0162 - mean_absolute_error: 0.0869 - val_loss: 0.0183 - val_mean_absolute_error: 0.0907
Epoch 8/50
383/383 [==============================] - 3s 8ms/step - loss: 0.0161 - mean_absolute_error: 0.0857 - val_loss: 0.0182 - val_mean_absolute_error: 0.0896
Epoch 9/50
383/383 [==============================] - 2s 4ms/step - loss: 0.0161 - mean_absolute_error: 0.0850 - val_loss: 0.0182 - val_mean_absolute_error: 0.0890
Epoch 10/50
383/383 [==============================] - 3s 9ms/step - loss: 0.0161 - mean_absolute_error: 0.0846 - val_loss: 0.0182 - val_mean_absolute_error: 0.0888
Epoch 11/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0161 - mean_absolute_error: 0.0843 - val_loss: 0.0182 - val_mean_absolute_error: 0.0887
Epoch 12/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0161 - mean_absolute_error: 0.0841 - val_loss: 0.0182 - val_mean_absolute_error: 0.0891
Epoch 13/50
383/383 [==============================] - 1s 4ms/step - loss: 0.0161 - mean_absolute_error: 0.0840 - val_loss: 0.0182 - val_mean_absolute_error: 0.0886
109/109 [==============================] - 0s 3ms/step - loss: 0.0182 - mean_absolute_error: 0.0886
54/54 [==============================] - 0s 5ms/step - loss: 0.0142 - mean_absolute_error: 0.0766
Epoch 1/50
383/383 [==============================] - 4s 8ms/step - loss: 0.0304 - mean_absolute_error: 0.1116 - val_loss: 0.0168 - val_mean_absolute_error: 0.0857
Epoch 2/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0142 - mean_absolute_error: 0.0789 - val_loss: 0.0161 - val_mean_absolute_error: 0.0833
Epoch 3/50
383/383 [==============================] - 3s 7ms/step - loss: 0.0138 - mean_absolute_error: 0.0784 - val_loss: 0.0156 - val_mean_absolute_error: 0.0823
Epoch 4/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0135 - mean_absolute_error: 0.0775 - val_loss: 0.0154 - val_mean_absolute_error: 0.0817
Epoch 5/50
383/383 [==============================] - 2s 6ms/step - loss: 0.0134 - mean_absolute_error: 0.0777 - val_loss: 0.0152 - val_mean_absolute_error: 0.0816
Epoch 6/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0131 - mean_absolute_error: 0.0767 - val_loss: 0.0149 - val_mean_absolute_error: 0.0829
Epoch 7/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0130 - mean_absolute_error: 0.0763 - val_loss: 0.0146 - val_mean_absolute_error: 0.0809
Epoch 8/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0130 - mean_absolute_error: 0.0761 - val_loss: 0.0146 - val_mean_absolute_error: 0.0812
Epoch 9/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0129 - mean_absolute_error: 0.0760 - val_loss: 0.0146 - val_mean_absolute_error: 0.0798
Epoch 10/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0129 - mean_absolute_error: 0.0759 - val_loss: 0.0147 - val_mean_absolute_error: 0.0804
Epoch 11/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0128 - mean_absolute_error: 0.0754 - val_loss: 0.0145 - val_mean_absolute_error: 0.0790
Epoch 12/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0128 - mean_absolute_error: 0.0756 - val_loss: 0.0148 - val_mean_absolute_error: 0.0800
Epoch 13/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0127 - mean_absolute_error: 0.0753 - val_loss: 0.0146 - val_mean_absolute_error: 0.0804
Epoch 14/50
383/383 [==============================] - 2s 5ms/step - loss: 0.0127 - mean_absolute_error: 0.0751 - val_loss: 0.0146 - val_mean_absolute_error: 0.0812
109/109 [==============================] - 0s 3ms/step - loss: 0.0146 - mean_absolute_error: 0.0812
54/54 [==============================] - 0s 3ms/step - loss: 0.0100 - mean_absolute_error: 0.0668
110/110 [==============================] - 1s 3ms/step - loss: 0.0069 - mean_absolute_error: 0.0482
110/110 [==============================] - 0s 3ms/step - loss: 0.0069 - mean_absolute_error: 0.0482
Epoch 1/50
384/384 [==============================] - 2s 4ms/step - loss: 0.1803 - mean_absolute_error: 0.3456 - val_loss: 0.0670 - val_mean_absolute_error: 0.2038
Epoch 2/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0444 - mean_absolute_error: 0.1666 - val_loss: 0.0279 - val_mean_absolute_error: 0.1320
Epoch 3/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0207 - mean_absolute_error: 0.1105 - val_loss: 0.0148 - val_mean_absolute_error: 0.0911
Epoch 4/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0119 - mean_absolute_error: 0.0790 - val_loss: 0.0095 - val_mean_absolute_error: 0.0684
Epoch 5/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0084 - mean_absolute_error: 0.0632 - val_loss: 0.0072 - val_mean_absolute_error: 0.0569
Epoch 6/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0069 - mean_absolute_error: 0.0557 - val_loss: 0.0061 - val_mean_absolute_error: 0.0508
Epoch 7/50
384/384 [==============================] - 3s 6ms/step - loss: 0.0061 - mean_absolute_error: 0.0518 - val_loss: 0.0056 - val_mean_absolute_error: 0.0478
Epoch 8/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0057 - mean_absolute_error: 0.0495 - val_loss: 0.0053 - val_mean_absolute_error: 0.0462
Epoch 9/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0055 - mean_absolute_error: 0.0478 - val_loss: 0.0051 - val_mean_absolute_error: 0.0450
Epoch 10/50
384/384 [==============================] - 4s 10ms/step - loss: 0.0053 - mean_absolute_error: 0.0463 - val_loss: 0.0049 - val_mean_absolute_error: 0.0439
Epoch 11/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0052 - mean_absolute_error: 0.0449 - val_loss: 0.0048 - val_mean_absolute_error: 0.0421
Epoch 12/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0051 - mean_absolute_error: 0.0435 - val_loss: 0.0048 - val_mean_absolute_error: 0.0406
Epoch 13/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0050 - mean_absolute_error: 0.0422 - val_loss: 0.0047 - val_mean_absolute_error: 0.0400
Epoch 14/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0050 - mean_absolute_error: 0.0412 - val_loss: 0.0047 - val_mean_absolute_error: 0.0387
Epoch 15/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0050 - mean_absolute_error: 0.0405 - val_loss: 0.0047 - val_mean_absolute_error: 0.0388
Epoch 16/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0049 - mean_absolute_error: 0.0399 - val_loss: 0.0046 - val_mean_absolute_error: 0.0383
Epoch 17/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0049 - mean_absolute_error: 0.0396 - val_loss: 0.0046 - val_mean_absolute_error: 0.0377
Epoch 18/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0049 - mean_absolute_error: 0.0395 - val_loss: 0.0046 - val_mean_absolute_error: 0.0380
Epoch 19/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0049 - mean_absolute_error: 0.0395 - val_loss: 0.0046 - val_mean_absolute_error: 0.0378
Epoch 20/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0049 - mean_absolute_error: 0.0395 - val_loss: 0.0046 - val_mean_absolute_error: 0.0375
Epoch 21/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0049 - mean_absolute_error: 0.0394 - val_loss: 0.0046 - val_mean_absolute_error: 0.0380
Epoch 22/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0049 - mean_absolute_error: 0.0395 - val_loss: 0.0046 - val_mean_absolute_error: 0.0381
110/110 [==============================] - 0s 3ms/step - loss: 0.0046 - mean_absolute_error: 0.0381
55/55 [==============================] - 0s 2ms/step - loss: 0.0043 - mean_absolute_error: 0.0360
Epoch 1/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0108 - mean_absolute_error: 0.0606 - val_loss: 0.0030 - val_mean_absolute_error: 0.0351
Epoch 2/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0029 - mean_absolute_error: 0.0333 - val_loss: 0.0024 - val_mean_absolute_error: 0.0324
Epoch 3/50
384/384 [==============================] - 2s 4ms/step - loss: 0.0023 - mean_absolute_error: 0.0300 - val_loss: 0.0018 - val_mean_absolute_error: 0.0282
Epoch 4/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0021 - mean_absolute_error: 0.0290 - val_loss: 0.0017 - val_mean_absolute_error: 0.0274
Epoch 5/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0021 - mean_absolute_error: 0.0284 - val_loss: 0.0023 - val_mean_absolute_error: 0.0315
Epoch 6/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0019 - mean_absolute_error: 0.0272 - val_loss: 0.0015 - val_mean_absolute_error: 0.0253
Epoch 7/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0019 - mean_absolute_error: 0.0271 - val_loss: 0.0015 - val_mean_absolute_error: 0.0248
Epoch 8/50
384/384 [==============================] - 3s 6ms/step - loss: 0.0019 - mean_absolute_error: 0.0267 - val_loss: 0.0016 - val_mean_absolute_error: 0.0260
Epoch 9/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0018 - mean_absolute_error: 0.0263 - val_loss: 0.0014 - val_mean_absolute_error: 0.0248
Epoch 10/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0018 - mean_absolute_error: 0.0262 - val_loss: 0.0014 - val_mean_absolute_error: 0.0248
Epoch 11/50
384/384 [==============================] - 4s 11ms/step - loss: 0.0018 - mean_absolute_error: 0.0258 - val_loss: 0.0016 - val_mean_absolute_error: 0.0260
Epoch 12/50
384/384 [==============================] - 5s 13ms/step - loss: 0.0017 - mean_absolute_error: 0.0255 - val_loss: 0.0014 - val_mean_absolute_error: 0.0236
Epoch 13/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0017 - mean_absolute_error: 0.0254 - val_loss: 0.0014 - val_mean_absolute_error: 0.0237
Epoch 14/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0017 - mean_absolute_error: 0.0248 - val_loss: 0.0013 - val_mean_absolute_error: 0.0235
Epoch 15/50
384/384 [==============================] - 2s 5ms/step - loss: 0.0017 - mean_absolute_error: 0.0249 - val_loss: 0.0013 - val_mean_absolute_error: 0.0230
Epoch 16/50
384/384 [==============================] - 3s 7ms/step - loss: 0.0016 - mean_absolute_error: 0.0248 - val_loss: 0.0014 - val_mean_absolute_error: 0.0238
Epoch 17/50
384/384 [==============================] - 2s 6ms/step - loss: 0.0017 - mean_absolute_error: 0.0248 - val_loss: 0.0014 - val_mean_absolute_error: 0.0250
Epoch 18/50
384/384 [==============================] - 3s 8ms/step - loss: 0.0016 - mean_absolute_error: 0.0243 - val_loss: 0.0014 - val_mean_absolute_error: 0.0246
110/110 [==============================] - 1s 4ms/step - loss: 0.0014 - mean_absolute_error: 0.0246
55/55 [==============================] - 0s 4ms/step - loss: 0.0010 - mean_absolute_error: 0.0217

リカレントニューラルネットワーク(RNN)を探索する¶

LSTMアーキテクチャを調べる¶

LSTMアーキテクチャを実装する¶

In [18]:

lstm_model = Sequential([
    LSTM(32, return_sequences=True),
    Dense(units=1)
])
history = compile_and_fit(lstm_model, wide_window)
val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)
test_performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)

lstm_model = Sequential([ LSTM(32, return_sequences=True), Dense(units=1) ]) history = compile_and_fit(lstm_model, wide_window) val_performance['LSTM'] = lstm_model.evaluate(wide_window.val) test_performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)

Epoch 1/50
384/384 [==============================] - 8s 16ms/step - loss: 0.0316 - mean_absolute_error: 0.1258 - val_loss: 0.0108 - val_mean_absolute_error: 0.0755
Epoch 2/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0072 - mean_absolute_error: 0.0607 - val_loss: 0.0050 - val_mean_absolute_error: 0.0506
Epoch 3/50
384/384 [==============================] - 9s 24ms/step - loss: 0.0046 - mean_absolute_error: 0.0477 - val_loss: 0.0038 - val_mean_absolute_error: 0.0437
Epoch 4/50
384/384 [==============================] - 13s 34ms/step - loss: 0.0039 - mean_absolute_error: 0.0432 - val_loss: 0.0033 - val_mean_absolute_error: 0.0401
Epoch 5/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0034 - mean_absolute_error: 0.0401 - val_loss: 0.0030 - val_mean_absolute_error: 0.0382
Epoch 6/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0032 - mean_absolute_error: 0.0383 - val_loss: 0.0028 - val_mean_absolute_error: 0.0370
Epoch 7/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0030 - mean_absolute_error: 0.0374 - val_loss: 0.0026 - val_mean_absolute_error: 0.0359
Epoch 8/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0029 - mean_absolute_error: 0.0368 - val_loss: 0.0025 - val_mean_absolute_error: 0.0351
Epoch 9/50
384/384 [==============================] - 9s 23ms/step - loss: 0.0028 - mean_absolute_error: 0.0363 - val_loss: 0.0025 - val_mean_absolute_error: 0.0350
Epoch 10/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0028 - mean_absolute_error: 0.0359 - val_loss: 0.0024 - val_mean_absolute_error: 0.0339
Epoch 11/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0027 - mean_absolute_error: 0.0355 - val_loss: 0.0025 - val_mean_absolute_error: 0.0348
Epoch 12/50
384/384 [==============================] - 7s 17ms/step - loss: 0.0026 - mean_absolute_error: 0.0350 - val_loss: 0.0024 - val_mean_absolute_error: 0.0345
Epoch 13/50
384/384 [==============================] - 7s 18ms/step - loss: 0.0026 - mean_absolute_error: 0.0347 - val_loss: 0.0023 - val_mean_absolute_error: 0.0336
Epoch 14/50
384/384 [==============================] - 7s 17ms/step - loss: 0.0025 - mean_absolute_error: 0.0343 - val_loss: 0.0022 - val_mean_absolute_error: 0.0327
Epoch 15/50
384/384 [==============================] - 7s 18ms/step - loss: 0.0025 - mean_absolute_error: 0.0341 - val_loss: 0.0022 - val_mean_absolute_error: 0.0326
Epoch 16/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0024 - mean_absolute_error: 0.0336 - val_loss: 0.0021 - val_mean_absolute_error: 0.0319
Epoch 17/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0024 - mean_absolute_error: 0.0333 - val_loss: 0.0021 - val_mean_absolute_error: 0.0330
Epoch 18/50
384/384 [==============================] - 9s 24ms/step - loss: 0.0024 - mean_absolute_error: 0.0330 - val_loss: 0.0020 - val_mean_absolute_error: 0.0309
Epoch 19/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0023 - mean_absolute_error: 0.0326 - val_loss: 0.0020 - val_mean_absolute_error: 0.0315
Epoch 20/50
384/384 [==============================] - 10s 25ms/step - loss: 0.0023 - mean_absolute_error: 0.0324 - val_loss: 0.0020 - val_mean_absolute_error: 0.0314
Epoch 21/50
384/384 [==============================] - 6s 17ms/step - loss: 0.0023 - mean_absolute_error: 0.0321 - val_loss: 0.0020 - val_mean_absolute_error: 0.0308
Epoch 22/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0022 - mean_absolute_error: 0.0320 - val_loss: 0.0020 - val_mean_absolute_error: 0.0325
Epoch 23/50
384/384 [==============================] - 10s 25ms/step - loss: 0.0022 - mean_absolute_error: 0.0317 - val_loss: 0.0019 - val_mean_absolute_error: 0.0309
Epoch 24/50
384/384 [==============================] - 7s 18ms/step - loss: 0.0022 - mean_absolute_error: 0.0315 - val_loss: 0.0019 - val_mean_absolute_error: 0.0300
Epoch 25/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0021 - mean_absolute_error: 0.0312 - val_loss: 0.0019 - val_mean_absolute_error: 0.0304
Epoch 26/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0021 - mean_absolute_error: 0.0310 - val_loss: 0.0018 - val_mean_absolute_error: 0.0295
Epoch 27/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0021 - mean_absolute_error: 0.0308 - val_loss: 0.0018 - val_mean_absolute_error: 0.0297
Epoch 28/50
384/384 [==============================] - 10s 25ms/step - loss: 0.0021 - mean_absolute_error: 0.0307 - val_loss: 0.0019 - val_mean_absolute_error: 0.0303
Epoch 29/50
384/384 [==============================] - 8s 22ms/step - loss: 0.0021 - mean_absolute_error: 0.0305 - val_loss: 0.0018 - val_mean_absolute_error: 0.0297
Epoch 30/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0020 - mean_absolute_error: 0.0303 - val_loss: 0.0018 - val_mean_absolute_error: 0.0298
Epoch 31/50
384/384 [==============================] - 11s 29ms/step - loss: 0.0020 - mean_absolute_error: 0.0301 - val_loss: 0.0018 - val_mean_absolute_error: 0.0296
Epoch 32/50
384/384 [==============================] - 8s 19ms/step - loss: 0.0020 - mean_absolute_error: 0.0299 - val_loss: 0.0017 - val_mean_absolute_error: 0.0288
Epoch 33/50
384/384 [==============================] - 7s 17ms/step - loss: 0.0020 - mean_absolute_error: 0.0299 - val_loss: 0.0019 - val_mean_absolute_error: 0.0310
Epoch 34/50
384/384 [==============================] - 7s 18ms/step - loss: 0.0020 - mean_absolute_error: 0.0297 - val_loss: 0.0017 - val_mean_absolute_error: 0.0289
Epoch 35/50
384/384 [==============================] - 6s 17ms/step - loss: 0.0019 - mean_absolute_error: 0.0295 - val_loss: 0.0017 - val_mean_absolute_error: 0.0291
109/109 [==============================] - 1s 7ms/step - loss: 0.0017 - mean_absolute_error: 0.0291

In [11]:

ms_lstm_model = Sequential([
    LSTM(32, return_sequences=True),
    Dense(units=1, kernel_initializer=tf.initializers.zeros)
])
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)

ms_lstm_model = Sequential([ LSTM(32, return_sequences=True), Dense(units=1, kernel_initializer=tf.initializers.zeros) ]) 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)

Epoch 1/50
383/383 [==============================] - 9s 19ms/step - loss: 0.0504 - mean_absolute_error: 0.1617 - val_loss: 0.0206 - val_mean_absolute_error: 0.0996
Epoch 2/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0169 - mean_absolute_error: 0.0894 - val_loss: 0.0181 - val_mean_absolute_error: 0.0922
Epoch 3/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0153 - mean_absolute_error: 0.0837 - val_loss: 0.0170 - val_mean_absolute_error: 0.0870
Epoch 4/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0147 - mean_absolute_error: 0.0811 - val_loss: 0.0165 - val_mean_absolute_error: 0.0851
Epoch 5/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0143 - mean_absolute_error: 0.0798 - val_loss: 0.0161 - val_mean_absolute_error: 0.0831
Epoch 6/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0140 - mean_absolute_error: 0.0787 - val_loss: 0.0157 - val_mean_absolute_error: 0.0826
Epoch 7/50
383/383 [==============================] - 11s 27ms/step - loss: 0.0137 - mean_absolute_error: 0.0778 - val_loss: 0.0155 - val_mean_absolute_error: 0.0811
Epoch 8/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0135 - mean_absolute_error: 0.0771 - val_loss: 0.0152 - val_mean_absolute_error: 0.0830
Epoch 9/50
383/383 [==============================] - 10s 25ms/step - loss: 0.0133 - mean_absolute_error: 0.0764 - val_loss: 0.0151 - val_mean_absolute_error: 0.0817
Epoch 10/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0132 - mean_absolute_error: 0.0758 - val_loss: 0.0150 - val_mean_absolute_error: 0.0815
Epoch 11/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0131 - mean_absolute_error: 0.0756 - val_loss: 0.0148 - val_mean_absolute_error: 0.0818
Epoch 12/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0130 - mean_absolute_error: 0.0752 - val_loss: 0.0147 - val_mean_absolute_error: 0.0785
Epoch 13/50
383/383 [==============================] - 10s 27ms/step - loss: 0.0129 - mean_absolute_error: 0.0746 - val_loss: 0.0148 - val_mean_absolute_error: 0.0784
Epoch 14/50
383/383 [==============================] - 10s 26ms/step - loss: 0.0128 - mean_absolute_error: 0.0743 - val_loss: 0.0146 - val_mean_absolute_error: 0.0808
Epoch 15/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0128 - mean_absolute_error: 0.0741 - val_loss: 0.0144 - val_mean_absolute_error: 0.0781
Epoch 16/50
383/383 [==============================] - 9s 25ms/step - loss: 0.0127 - mean_absolute_error: 0.0740 - val_loss: 0.0145 - val_mean_absolute_error: 0.0787
Epoch 17/50
383/383 [==============================] - 9s 23ms/step - loss: 0.0126 - mean_absolute_error: 0.0734 - val_loss: 0.0143 - val_mean_absolute_error: 0.0786
Epoch 18/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0124 - mean_absolute_error: 0.0728 - val_loss: 0.0141 - val_mean_absolute_error: 0.0782
Epoch 19/50
383/383 [==============================] - 11s 28ms/step - loss: 0.0124 - mean_absolute_error: 0.0725 - val_loss: 0.0141 - val_mean_absolute_error: 0.0765
Epoch 20/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0123 - mean_absolute_error: 0.0720 - val_loss: 0.0139 - val_mean_absolute_error: 0.0787
Epoch 21/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0121 - mean_absolute_error: 0.0715 - val_loss: 0.0141 - val_mean_absolute_error: 0.0775
Epoch 22/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0120 - mean_absolute_error: 0.0712 - val_loss: 0.0139 - val_mean_absolute_error: 0.0770
Epoch 23/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0120 - mean_absolute_error: 0.0711 - val_loss: 0.0138 - val_mean_absolute_error: 0.0777
Epoch 24/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0119 - mean_absolute_error: 0.0707 - val_loss: 0.0135 - val_mean_absolute_error: 0.0753
Epoch 25/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0118 - mean_absolute_error: 0.0704 - val_loss: 0.0134 - val_mean_absolute_error: 0.0751
Epoch 26/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0117 - mean_absolute_error: 0.0698 - val_loss: 0.0132 - val_mean_absolute_error: 0.0752
Epoch 27/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0116 - mean_absolute_error: 0.0695 - val_loss: 0.0132 - val_mean_absolute_error: 0.0710
Epoch 28/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0114 - mean_absolute_error: 0.0690 - val_loss: 0.0127 - val_mean_absolute_error: 0.0718
Epoch 29/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0114 - mean_absolute_error: 0.0689 - val_loss: 0.0127 - val_mean_absolute_error: 0.0716
Epoch 30/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0111 - mean_absolute_error: 0.0679 - val_loss: 0.0126 - val_mean_absolute_error: 0.0717
Epoch 31/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0109 - mean_absolute_error: 0.0674 - val_loss: 0.0122 - val_mean_absolute_error: 0.0704
Epoch 32/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0109 - mean_absolute_error: 0.0670 - val_loss: 0.0123 - val_mean_absolute_error: 0.0686
Epoch 33/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0108 - mean_absolute_error: 0.0667 - val_loss: 0.0122 - val_mean_absolute_error: 0.0714
Epoch 34/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0106 - mean_absolute_error: 0.0660 - val_loss: 0.0121 - val_mean_absolute_error: 0.0686
Epoch 35/50
383/383 [==============================] - 8s 19ms/step - loss: 0.0105 - mean_absolute_error: 0.0658 - val_loss: 0.0120 - val_mean_absolute_error: 0.0690
Epoch 36/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0105 - mean_absolute_error: 0.0655 - val_loss: 0.0117 - val_mean_absolute_error: 0.0688
Epoch 37/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0104 - mean_absolute_error: 0.0655 - val_loss: 0.0117 - val_mean_absolute_error: 0.0692
Epoch 38/50
383/383 [==============================] - 8s 21ms/step - loss: 0.0104 - mean_absolute_error: 0.0651 - val_loss: 0.0118 - val_mean_absolute_error: 0.0663
Epoch 39/50
383/383 [==============================] - 8s 20ms/step - loss: 0.0103 - mean_absolute_error: 0.0646 - val_loss: 0.0117 - val_mean_absolute_error: 0.0670
Epoch 40/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0103 - mean_absolute_error: 0.0646 - val_loss: 0.0124 - val_mean_absolute_error: 0.0699
Epoch 41/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0102 - mean_absolute_error: 0.0644 - val_loss: 0.0115 - val_mean_absolute_error: 0.0688
Epoch 42/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0102 - mean_absolute_error: 0.0645 - val_loss: 0.0119 - val_mean_absolute_error: 0.0708
Epoch 43/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0101 - mean_absolute_error: 0.0640 - val_loss: 0.0115 - val_mean_absolute_error: 0.0683
Epoch 44/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0101 - mean_absolute_error: 0.0638 - val_loss: 0.0115 - val_mean_absolute_error: 0.0682
Epoch 45/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0101 - mean_absolute_error: 0.0636 - val_loss: 0.0116 - val_mean_absolute_error: 0.0710
Epoch 46/50
383/383 [==============================] - 7s 19ms/step - loss: 0.0100 - mean_absolute_error: 0.0635 - val_loss: 0.0115 - val_mean_absolute_error: 0.0671
Epoch 47/50
383/383 [==============================] - 7s 18ms/step - loss: 0.0100 - mean_absolute_error: 0.0634 - val_loss: 0.0115 - val_mean_absolute_error: 0.0689
109/109 [==============================] - 1s 8ms/step - loss: 0.0115 - mean_absolute_error: 0.0689
54/54 [==============================] - 0s 7ms/step - loss: 0.0082 - mean_absolute_error: 0.0551

In [12]:

mo_lstm_model = Sequential([
    LSTM(32, return_sequences=True),
    Dense(units=2)
])
history = compile_and_fit(mo_lstm_model, mo_wide_window)
mo_val_performance['LSTM'] = mo_lstm_model.evaluate(mo_wide_window.val)
mo_test_performance['LSTM'] = mo_lstm_model.evaluate(mo_wide_window.test)

mo_lstm_model = Sequential([ LSTM(32, return_sequences=True), Dense(units=2) ]) history = compile_and_fit(mo_lstm_model, mo_wide_window) mo_val_performance['LSTM'] = mo_lstm_model.evaluate(mo_wide_window.val) mo_test_performance['LSTM'] = mo_lstm_model.evaluate(mo_wide_window.test)

Epoch 1/50
384/384 [==============================] - 9s 20ms/step - loss: 0.0354 - mean_absolute_error: 0.1212 - val_loss: 0.0121 - val_mean_absolute_error: 0.0698
Epoch 2/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0073 - mean_absolute_error: 0.0555 - val_loss: 0.0051 - val_mean_absolute_error: 0.0450
Epoch 3/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0041 - mean_absolute_error: 0.0409 - val_loss: 0.0033 - val_mean_absolute_error: 0.0373
Epoch 4/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0031 - mean_absolute_error: 0.0354 - val_loss: 0.0026 - val_mean_absolute_error: 0.0328
Epoch 5/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0027 - mean_absolute_error: 0.0327 - val_loss: 0.0022 - val_mean_absolute_error: 0.0303
Epoch 6/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0025 - mean_absolute_error: 0.0308 - val_loss: 0.0020 - val_mean_absolute_error: 0.0289
Epoch 7/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0023 - mean_absolute_error: 0.0295 - val_loss: 0.0019 - val_mean_absolute_error: 0.0279
Epoch 8/50
384/384 [==============================] - 7s 19ms/step - loss: 0.0021 - mean_absolute_error: 0.0283 - val_loss: 0.0018 - val_mean_absolute_error: 0.0270
Epoch 9/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0020 - mean_absolute_error: 0.0273 - val_loss: 0.0017 - val_mean_absolute_error: 0.0261
Epoch 10/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0019 - mean_absolute_error: 0.0266 - val_loss: 0.0016 - val_mean_absolute_error: 0.0257
Epoch 11/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0018 - mean_absolute_error: 0.0258 - val_loss: 0.0015 - val_mean_absolute_error: 0.0245
Epoch 12/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0018 - mean_absolute_error: 0.0251 - val_loss: 0.0015 - val_mean_absolute_error: 0.0243
Epoch 13/50
384/384 [==============================] - 9s 24ms/step - loss: 0.0017 - mean_absolute_error: 0.0246 - val_loss: 0.0014 - val_mean_absolute_error: 0.0240
Epoch 14/50
384/384 [==============================] - 8s 22ms/step - loss: 0.0017 - mean_absolute_error: 0.0241 - val_loss: 0.0014 - val_mean_absolute_error: 0.0237
Epoch 15/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0016 - mean_absolute_error: 0.0238 - val_loss: 0.0014 - val_mean_absolute_error: 0.0236
Epoch 16/50
384/384 [==============================] - 9s 23ms/step - loss: 0.0016 - mean_absolute_error: 0.0236 - val_loss: 0.0013 - val_mean_absolute_error: 0.0228
Epoch 17/50
384/384 [==============================] - 8s 21ms/step - loss: 0.0016 - mean_absolute_error: 0.0234 - val_loss: 0.0013 - val_mean_absolute_error: 0.0226
Epoch 18/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0016 - mean_absolute_error: 0.0231 - val_loss: 0.0013 - val_mean_absolute_error: 0.0229
Epoch 19/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0015 - mean_absolute_error: 0.0229 - val_loss: 0.0013 - val_mean_absolute_error: 0.0226
Epoch 20/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0015 - mean_absolute_error: 0.0228 - val_loss: 0.0012 - val_mean_absolute_error: 0.0219
Epoch 21/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0015 - mean_absolute_error: 0.0227 - val_loss: 0.0012 - val_mean_absolute_error: 0.0216
Epoch 22/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0015 - mean_absolute_error: 0.0226 - val_loss: 0.0012 - val_mean_absolute_error: 0.0219
Epoch 23/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0015 - mean_absolute_error: 0.0224 - val_loss: 0.0012 - val_mean_absolute_error: 0.0221
Epoch 24/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0014 - mean_absolute_error: 0.0224 - val_loss: 0.0012 - val_mean_absolute_error: 0.0217
Epoch 25/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0014 - mean_absolute_error: 0.0222 - val_loss: 0.0011 - val_mean_absolute_error: 0.0209
Epoch 26/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0014 - mean_absolute_error: 0.0220 - val_loss: 0.0011 - val_mean_absolute_error: 0.0211
Epoch 27/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0014 - mean_absolute_error: 0.0219 - val_loss: 0.0012 - val_mean_absolute_error: 0.0212
Epoch 28/50
384/384 [==============================] - 8s 20ms/step - loss: 0.0014 - mean_absolute_error: 0.0217 - val_loss: 0.0012 - val_mean_absolute_error: 0.0220
109/109 [==============================] - 1s 8ms/step - loss: 0.0012 - mean_absolute_error: 0.0220
55/55 [==============================] - 0s 8ms/step - loss: 9.0453e-04 - mean_absolute_error: 0.0194

In [19]:

# single stepの結果比較
plt.title('Single Step')
plt.xlabel('Models')
plt.ylabel('MAE')
plt.bar(val_performance.keys(), [v[1] for v in val_performance.values()], width=-0.25, align='edge', label='Validation')
plt.bar(test_performance.keys(), [v[1] for v in test_performance.values()], width=0.25, align='edge', label='Test', hatch='/')
plt.legend()
plt.tight_layout()

# single stepの結果比較 plt.title('Single Step') plt.xlabel('Models') plt.ylabel('MAE') plt.bar(val_performance.keys(), [v[1] for v in val_performance.values()], width=-0.25, align='edge', label='Validation') plt.bar(test_performance.keys(), [v[1] for v in test_performance.values()], width=0.25, align='edge', label='Test', hatch='/') plt.legend() plt.tight_layout()

In [14]:

# 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.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.tight_layout()

In [15]:

# multi outputの結果比較
plt.title('Multi Output')
plt.xlabel('Models')
plt.ylabel('MAE')
plt.bar(mo_val_performance.keys(), [v[1] for v in mo_val_performance.values()], width=-0.25, align='edge', label='Validation')
plt.bar(mo_test_performance.keys(), [v[1] for v in mo_test_performance.values()], width=0.25, align='edge', label='Test', hatch='/')
plt.legend()
plt.tight_layout()

# multi outputの結果比較 plt.title('Multi Output') plt.xlabel('Models') plt.ylabel('MAE') plt.bar(mo_val_performance.keys(), [v[1] for v in mo_val_performance.values()], width=-0.25, align='edge', label='Validation') plt.bar(mo_test_performance.keys(), [v[1] for v in mo_test_performance.values()], width=0.25, align='edge', label='Test', hatch='/') plt.legend() plt.tight_layout()

In [ ]: