はじめに
Hirofumi Yashimaさんの投稿された
Keras 1d-CNN １次元畳み込みニューラルネットワーク で 単変量回帰タスク を 行って成功した件
について、Keras Conv1DのInput Shapeの順番はChannel firstかChannel lastのどちらが正解かを議論するためのメモです
私の環境について
OS windows10 Home
Python 3.5.3 Anaconda 64-bit
tensorflow 1.0.0
Keras 2.0.2
.keras.json
{
"image_dim_ordering": "th",
"backend": "tensorflow",
"epsilon": 1e-07,
"floatx": "float32"
}
なので、まずKerasの環境が1系か2系で挙動が異なる可能性があります。
Channel first or last?
以下の通り、私はChannel firstになっているとコメントしました。
なるほど、ありがとうございます！
backendがtensorflowだけですが、ソースコードでもご指摘の通りでした。。https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py#L2819
tensorflow_backend.py#L2819
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
if data_format == 'channels_last':
tf_data_format = 'NWC'
else:
tf_data_format = 'NCW'
デフォルトのdata_formatがNoneになっているので、tf_data_formatはNCW
つまり、(None, 変数の種類, 3期分のデータ)になるんですね。
しかし、Conv1D他の部分も調べてみるとConv1Dの初期化の際、data_format='channels_last'に設定されています。（superの挙動をいまいち理解できていません。。。）
convolutional.py#L233
class Conv1D(_Conv):
.
.
super(Conv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format='channels_last',
Conv1Dを呼び出す際、data_formatが設定されているのでdata_formatはchannels_lastになっていると思います。
convolutional.py#L148
def call(self, inputs):
if self.rank == 1:
outputs = K.conv1d(
inputs,
self.kernel,
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
つまり、input shapeの順番はchannel last(None, データサイズ, 変数の種類数)が正しいのではないかと思います。
また、hayatoyさんが投稿されたTensorFlow (ディープラーニング)で為替(FX)の予測をしてみる CNN編の記事にて、1種類、24日分データを入力を以下のようにしていることから、channel lastになっているのではなかと思います。
CNN
input_shape = (24, 1)
そして、プログラムでも確認してみました。
プログラムの説明
DATA_SAMPLESがサンプルサイズ、CHANNELSが変数の数、DATA_LENGTHがデータの長さを示しています。
具体的に言えば、１次元の１０か月データを２０セット用意して、サイズ３の５０個あるフィルタで１次元のCNNを行うプログラムです。
Conv1Dの処理後の出力としては、PADDING=sameにしているので、入力と同じデータの10とフィルタ数が50が出力されるはずです。
channel firstのConv1D出力は
conv1d_1 (Conv1D) (None, 1, 50)
channel lastのConv1D出力は
conv1d_2 (Conv1D) (None, 10, 50)
channel firstのConv1Dはデータ数が1個になり、フィルタ数50個が出力されています。
channel lastのConv1Dは期待通り、データ数が10、フィルタ数50となっています。
import numpy as np
from keras.models import Model, Sequential
from keras.layers import Dense, Activation, Flatten, Conv1D, Input
from keras.utils import np_utils
from keras.utils.vis_utils import plot_model
from keras.optimizers import Adam
import pydot_ng as pydot
Using TensorFlow backend.
DATA_SAMPLES = 20
CHANNELS = 1
DATA_LENGTH = 10
KERNEL_SIZE = 3
FILTERS = 50
data = np.arange(DATA_SAMPLES * CHANNELS * DATA_LENGTH) * 10
data_channels_first = data.reshape(DATA_SAMPLES, CHANNELS, DATA_LENGTH)
data_channels_last = data_channels_first.transpose(0,2,1)
seikai = np.arange(DATA_SAMPLES).reshape(-1, 1)
print(data_channels_first.shape, data_channels_last.shape)
#print(data_channels_first)
#print(data_channels_last)
(20, 1, 10) (20, 10, 1)
def make_model(shape):
input_layer = Input(batch_shape=shape)
conv_1d_output_layer = Conv1D(FILTERS, KERNEL_SIZE, padding='same')(input_layer)
flatten_output_layer = Flatten()(conv_1d_output_layer)
prediction_result = Dense(1)(flatten_output_layer)
model = Model(inputs=input_layer, outputs=prediction_result)
return model
model_channels_first = make_model(shape=(None, CHANNELS, DATA_LENGTH))
model_channels_last = make_model(shape=(None, DATA_LENGTH, CHANNELS))
model_channels_first.compile(optimizer="adam", loss='mse', metrics=['accuracy'])
print('channels first model')
print(model_channels_first.summary())
print('\n\n')
model_channels_last.compile(optimizer="adam", loss='mse', metrics=['accuracy'])
print('channels last model')
print(model_channels_last.summary())
channels first model
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 1, 10) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 1, 50) 1550
_________________________________________________________________
flatten_1 (Flatten) (None, 50) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 51
=================================================================
Total params: 1,601.0
Trainable params: 1,601.0
Non-trainable params: 0.0
_________________________________________________________________
None
channels last model
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) (None, 10, 1) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 10, 50) 200
_________________________________________________________________
flatten_2 (Flatten) (None, 500) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 501
=================================================================
Total params: 701.0
Trainable params: 701.0
Non-trainable params: 0.0
_________________________________________________________________
None
model_channels_first.fit(data_channels_first, seikai, batch_size=600, epochs=200)
Epoch 1/200
20/20 [==============================] - 1s - loss: 30414.6836 - acc: 0.0000e+00
Epoch 2/200
20/20 [==============================] - 0s - loss: 9868.0898 - acc: 0.0000e+00
Epoch 3/200
20/20 [==============================] - 0s - loss: 312.4087 - acc: 0.0500
Epoch 4/200
20/20 [==============================] - 0s - loss: 3033.8848 - acc: 0.0000e+00
Epoch 5/200
20/20 [==============================] - 0s - loss: 9813.7725 - acc: 0.0000e+00
Epoch 6/200
20/20 [==============================] - 0s - loss: 12278.8740 - acc: 0.0000e+00
Epoch 7/200
20/20 [==============================] - 0s - loss: 9633.3096 - acc: 0.0000e+00
Epoch 8/200
20/20 [==============================] - 0s - loss: 4913.5479 - acc: 0.0000e+00
Epoch 9/200
20/20 [==============================] - 0s - loss: 1164.7600 - acc: 0.0000e+00
Epoch 10/200
20/20 [==============================] - 0s - loss: 36.5087 - acc: 0.0500
省略
Epoch 190/200
20/20 [==============================] - 0s - loss: 17.9575 - acc: 0.0500
Epoch 191/200
20/20 [==============================] - 0s - loss: 17.9285 - acc: 0.0500
Epoch 192/200
20/20 [==============================] - 0s - loss: 17.8995 - acc: 0.0500
Epoch 193/200
20/20 [==============================] - 0s - loss: 17.8703 - acc: 0.0500
Epoch 194/200
20/20 [==============================] - 0s - loss: 17.8411 - acc: 0.0500
Epoch 195/200
20/20 [==============================] - 0s - loss: 17.8119 - acc: 0.0500
Epoch 196/200
20/20 [==============================] - 0s - loss: 17.7828 - acc: 0.0500
Epoch 197/200
20/20 [==============================] - 0s - loss: 17.7535 - acc: 0.0500
Epoch 198/200
20/20 [==============================] - 0s - loss: 17.7242 - acc: 0.0500
Epoch 199/200
20/20 [==============================] - 0s - loss: 17.6949 - acc: 0.0500
Epoch 200/200
20/20 [==============================] - 0s - loss: 17.6655 - acc: 0.0500
<keras.callbacks.History at 0x1d1bf2cd240>
model_channels_last.fit(data_channels_last, seikai, batch_size=600, epochs=200)
Epoch 1/200
20/20 [==============================] - 0s - loss: 969.5367 - acc: 0.0000e+00
Epoch 2/200
20/20 [==============================] - 0s - loss: 2515.6497 - acc: 0.0000e+00
Epoch 3/200
20/20 [==============================] - 0s - loss: 575.2820 - acc: 0.0000e+00
Epoch 4/200
20/20 [==============================] - 0s - loss: 387.8024 - acc: 0.0500
Epoch 5/200
20/20 [==============================] - 0s - loss: 1396.0237 - acc: 0.0000e+00
Epoch 6/200
20/20 [==============================] - 0s - loss: 523.0975 - acc: 0.0500
Epoch 7/200
20/20 [==============================] - 0s - loss: 25.1633 - acc: 0.0000e+00
Epoch 8/200
20/20 [==============================] - 0s - loss: 682.5659 - acc: 0.0000e+00
Epoch 9/200
20/20 [==============================] - 0s - loss: 753.8232 - acc: 0.0000e+00
Epoch 10/200
20/20 [==============================] - 0s - loss: 151.1371 - acc: 0.0000e+00
Epoch 11/200
20/20 [==============================] - 0s - loss: 69.5525 - acc: 0.0500
省略
Epoch 190/200
20/20 [==============================] - 0s - loss: 0.0113 - acc: 1.0000
Epoch 191/200
20/20 [==============================] - 0s - loss: 0.0112 - acc: 1.0000
Epoch 192/200
20/20 [==============================] - 0s - loss: 0.0110 - acc: 1.0000
Epoch 193/200
20/20 [==============================] - 0s - loss: 0.0109 - acc: 1.0000
Epoch 194/200
20/20 [==============================] - 0s - loss: 0.0107 - acc: 1.0000
Epoch 195/200
20/20 [==============================] - 0s - loss: 0.0106 - acc: 1.0000
Epoch 196/200
20/20 [==============================] - 0s - loss: 0.0104 - acc: 1.0000
Epoch 197/200
20/20 [==============================] - 0s - loss: 0.0103 - acc: 1.0000
Epoch 198/200
20/20 [==============================] - 0s - loss: 0.0101 - acc: 1.0000
Epoch 199/200
20/20 [==============================] - 0s - loss: 0.0100 - acc: 1.0000
Epoch 200/200
20/20 [==============================] - 0s - loss: 0.0098 - acc: 1.0000
<keras.callbacks.History at 0x1d33647eef0>
predicted_channels_first = model_channels_first.predict(data_channels_first)
predicted_channels_last = model_channels_last.predict(data_channels_last)
predicted_channels_first
array([[ 8.09207153],
[ 8.46234894],
[ 8.83262348],
[ 9.20289421],
[ 9.57317162],
[ 9.94345951],
[ 10.31372738],
[ 10.6840086 ],
[ 11.05428028],
[ 11.42456627],
[ 11.79482174],
[ 12.16512108],
[ 12.53540611],
[ 12.90565395],
[ 13.2759409 ],
[ 13.64623451],
[ 14.01647854],
[ 14.38675022],
[ 14.7570734 ],
[ 15.12734604]], dtype=float32)
predicted_channels_last
array([[ 0.18978588],
[ 1.17511916],
[ 2.16048503],
[ 3.14581513],
[ 4.13118029],
[ 5.11650562],
[ 6.10181856],
[ 7.08713865],
[ 8.07252884],
[ 9.05784416],
[ 10.04322052],
[ 11.02850723],
[ 12.01392174],
[ 12.99925709],
[ 13.98452473],
[ 14.96988583],
[ 15.95536232],
[ 16.94049072],
[ 17.92590523],
[ 18.91131401]], dtype=float32)
最後に
いつも色々な情報提供して下さるHirofumi Yashimaさんに多謝です。