回答編集履歴
8
fix answer
test
CHANGED
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@@ -68,7 +68,7 @@
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68
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epochs = epochs
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69
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)
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70
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```
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71
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-
基本，分類問題を解かせる場合はTrainable params
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71
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+
基本，分類問題を解かせる場合はTrainable paramsやユニット数を出力側に行くにつれ減少させるのが常套手段です．カーネルの枚数を徐々に減少させるようにしておきました．また，活性化関数もELUを上回った[Swish](https://arxiv.org/pdf/1710.05941v1.pdf)を推奨しておきます．さらに，カーネルの初期値は`glorot_uniform`なので，ELUやSwishのような[ReLUファミリ用の`he_uniform`](https://arxiv.org/pdf/1502.01852.pdf)を推奨します．
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72
72
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```Python:network.summary()
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73
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Model: "model"
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74
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__________________________________________________________________________________________________
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7
fix result on feature shape (2,)
test
CHANGED
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@@ -100,22 +100,23 @@
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100
100
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flatten (Flatten) (None, 256) 0 ['max_pooling2d_4[0][0]']
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102
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-
feature_input (InputLayer) [(None,
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+
feature_input (InputLayer) [(None, 2)] 0 []
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104
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105
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-
concatenate (Concatenate) (None, 2
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105
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+
concatenate (Concatenate) (None, 258) 0 ['flatten[0][0]',
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'feature_input[0][0]']
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107
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-
dropout (Dropout) (None, 2
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+
dropout (Dropout) (None, 258) 0 ['concatenate[0][0]']
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109
109
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-
dense (Dense) (None, 128) 335
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+
dense (Dense) (None, 128) 33152 ['dropout[0][0]']
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dense_1 (Dense) (None, 10) 1290 ['dense[0][0]']
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==================================================================================================
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115
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-
Total params: 47,
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+
Total params: 47,132
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-
Trainable params: 47,
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116
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+
Trainable params: 47,132
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Non-trainable params: 0
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+
__________________________________________________________________________________________________
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```
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-
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個人的には，CNNだけを分類予測学習した後，CNNを学習しないようにして特徴量マップ出力と追加情報を合併して予測/分類する方が良いと思います．
|
6
append network summary
test
CHANGED
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@@ -69,6 +69,53 @@
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69
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)
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70
70
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```
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71
71
|
基本，分類問題を解かせる場合はTrainable paramsは出力側に行くにつれ減少させるのが常套手段です．カーネルの枚数を徐々に減少させるようにしておきました．また，活性化関数もELUを上回った[Swish](https://arxiv.org/pdf/1710.05941v1.pdf)を推奨しておきます．さらに，カーネルの初期値は`glorot_uniform`なので，ELUやSwishのような[ReLUファミリ用の`he_uniform`](https://arxiv.org/pdf/1502.01852.pdf)を推奨します．
|
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72
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+
```Python:network.summary()
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73
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+
Model: "model"
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74
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+
__________________________________________________________________________________________________
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75
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+
Layer (type) Output Shape Param # Connected to
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76
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+
==================================================================================================
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77
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+
image_input (InputLayer) [(None, 256, 128, 1 0 []
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+
)]
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79
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+
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80
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+
conv2d (Conv2D) (None, 256, 128, 32 320 ['image_input[0][0]']
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81
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+
)
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82
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+
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83
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+
max_pooling2d (MaxPooling2D) (None, 128, 64, 32) 0 ['conv2d[0][0]']
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84
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+
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85
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+
conv2d_1 (Conv2D) (None, 128, 64, 23) 6647 ['max_pooling2d[0][0]']
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86
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+
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87
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+
max_pooling2d_1 (MaxPooling2D) (None, 64, 32, 23) 0 ['conv2d_1[0][0]']
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88
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+
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89
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+
conv2d_2 (Conv2D) (None, 64, 32, 16) 3328 ['max_pooling2d_1[0][0]']
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90
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+
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91
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+
max_pooling2d_2 (MaxPooling2D) (None, 32, 16, 16) 0 ['conv2d_2[0][0]']
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92
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+
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93
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+
conv2d_3 (Conv2D) (None, 32, 16, 11) 1595 ['max_pooling2d_2[0][0]']
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94
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+
|
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95
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+
max_pooling2d_3 (MaxPooling2D) (None, 16, 8, 11) 0 ['conv2d_3[0][0]']
|
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96
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+
|
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97
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+
conv2d_4 (Conv2D) (None, 16, 8, 8) 800 ['max_pooling2d_3[0][0]']
|
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98
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+
|
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99
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+
max_pooling2d_4 (MaxPooling2D) (None, 8, 4, 8) 0 ['conv2d_4[0][0]']
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100
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+
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101
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+
flatten (Flatten) (None, 256) 0 ['max_pooling2d_4[0][0]']
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102
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+
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103
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+
feature_input (InputLayer) [(None, 5)] 0 []
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104
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+
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105
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+
concatenate (Concatenate) (None, 261) 0 ['flatten[0][0]',
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106
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+
'feature_input[0][0]']
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107
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+
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108
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+
dropout (Dropout) (None, 261) 0 ['concatenate[0][0]']
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109
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+
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110
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+
dense (Dense) (None, 128) 33536 ['dropout[0][0]']
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111
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+
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112
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+
dense_1 (Dense) (None, 10) 1290 ['dense[0][0]']
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113
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+
|
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114
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+
==================================================================================================
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115
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+
Total params: 47,516
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|
116
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+
Trainable params: 47,516
|
|
117
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+
Non-trainable params: 0
|
|
118
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+
```
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72
119
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73
120
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|
|
74
121
|
個人的には，CNNだけを分類予測学習した後，CNNを学習しないようにして特徴量マップ出力と追加情報を合併して予測/分類する方が良いと思います．
|
5
append code comments
test
CHANGED
|
@@ -8,9 +8,9 @@
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8
8
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import numpy as np
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9
9
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10
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image_input = Input(shape = (256, 128, 1), name = "image_input")
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11
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-
append_input = Input(shape = (2,), name = "feature_input") # 追加情報の特徴量の数だけ
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11
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+
append_input = Input(shape = (2,), name = "feature_input") # 追加情報の特徴量の数だけ次元数を指定する
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12
12
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13
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-
params = { # 同一のパラメータはまとめて書いておく
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13
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+
params = { # 同一のパラメータは まとめて書いておく
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14
14
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"kernel_size": (3, 3),
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15
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"strides": (1, 1),
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16
16
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"padding": "same",
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@@ -37,21 +37,29 @@
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37
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network = Model(inputs = [image_input, append_input], outputs = x)
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39
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40
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+
network.compile(
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+
optimizer = 'adam',
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40
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-
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42
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+
loss = 'categorical_crossentropy',
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+
metrics=['accuracy']
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44
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+
)
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41
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network.summary()
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43
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from keras.utils.vis_utils import plot_model
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44
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plot_model(network, to_file = "CNN.png", rankdir = "LR", show_shapes = True, show_layer_names = True, show_layer_activations = True)
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45
49
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50
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+
# 擬似データを用意
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51
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+
# 擬似画像 32枚解像度256x128のグレースケール画像
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46
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train_img = np.random.randn(32, 256, 128, 1)
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47
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-
#
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53
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+
# train_feautre = np.array([ [x0, y0], [x1, y1], [x2, y2], ..., [xn, yn] ]) となるように与える
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48
54
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train_feature = np.random.randn(32, 2)
|
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55
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+
# モデルに出力してほしい値．One-Hot Encoding済であること．
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49
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train_y = np.abs(np.random.randn(32, 10))
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57
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+
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50
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batch_size = 32
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51
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epochs = 10
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52
60
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53
61
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network.fit(
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54
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-
x = { # レイヤの名前と一致させる
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62
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+
x = { # keyはレイヤの名前と一致させる
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55
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"image_input": train_img,
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"feature_input": train_feature
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},
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4
fix answer
test
CHANGED
|
@@ -8,7 +8,7 @@
|
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8
8
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import numpy as np
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9
9
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10
10
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image_input = Input(shape = (256, 128, 1), name = "image_input")
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11
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-
append_input = Input(shape = (
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11
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+
append_input = Input(shape = (2,), name = "feature_input") # 追加情報の特徴量の数だけ
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12
12
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13
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params = { # 同一のパラメータはまとめて書いておく
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"kernel_size": (3, 3),
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@@ -44,7 +44,8 @@
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44
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plot_model(network, to_file = "CNN.png", rankdir = "LR", show_shapes = True, show_layer_names = True, show_layer_activations = True)
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45
45
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46
46
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train_img = np.random.randn(32, 256, 128, 1)
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47
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+
# 擬似的に train_feautre = np.array([ [x0, y0], [x1, y1], [x2, y2], ..., [xn, yn] ])
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47
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-
train_feature = np.random.randn(32,
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48
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+
train_feature = np.random.randn(32, 2)
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48
49
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train_y = np.abs(np.random.randn(32, 10))
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batch_size = 32
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epochs = 10
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3
fix answer
test
CHANGED
|
@@ -59,7 +59,7 @@
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59
59
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epochs = epochs
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60
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)
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61
61
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```
|
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62
|
-
基本，分類問題を解かせる場合はTrainable paramsは出力側に行くにつれ減少させるのが
|
|
62
|
+
基本，分類問題を解かせる場合はTrainable paramsは出力側に行くにつれ減少させるのが常套手段です．カーネルの枚数を徐々に減少させるようにしておきました．また，活性化関数もELUを上回った[Swish](https://arxiv.org/pdf/1710.05941v1.pdf)を推奨しておきます．さらに，カーネルの初期値は`glorot_uniform`なので，ELUやSwishのような[ReLUファミリ用の`he_uniform`](https://arxiv.org/pdf/1502.01852.pdf)を推奨します．
|
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63
63
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64
64
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65
65
|
個人的には，CNNだけを分類予測学習した後，CNNを学習しないようにして特徴量マップ出力と追加情報を合併して予測/分類する方が良いと思います．
|
2
fix code
test
CHANGED
|
@@ -10,26 +10,34 @@
|
|
|
10
10
|
image_input = Input(shape = (256, 128, 1), name = "image_input")
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11
11
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append_input = Input(shape = (5), name = "feature_input") # 追加情報の特徴量の数だけ
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12
12
|
|
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13
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+
params = { # 同一のパラメータはまとめて書いておく
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14
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+
"kernel_size": (3, 3),
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15
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+
"strides": (1, 1),
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16
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+
"padding": "same",
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17
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+
"activation": "swish", # 連続関数を利用する
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13
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-
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18
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+
"kernel_initializer": "he_normal" # ReLUファミリ用の活性化関数を利用する
|
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19
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+
}
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20
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+
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21
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+
x = Conv2D(filters=32, **params)(image_input)
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14
22
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x = MaxPool2D(pool_size=(2,2), strides=None, padding='same')(x)
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15
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-
x = Conv2D(filters=
|
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+
x = Conv2D(filters=23, **params)(x)
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16
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x = MaxPool2D(pool_size=(2,2), strides=None, padding='same')(x)
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17
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-
x = Conv2D(filters=16,
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+
x = Conv2D(filters=16, **params)(x)
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x = MaxPool2D(pool_size=(2,2), strides=None, padding='same')(x)
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-
x = Conv2D(filters=1
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+
x = Conv2D(filters=11, **params)(x)
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20
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x = MaxPool2D(pool_size=(2,2), strides=None, padding='same')(x)
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21
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-
x = Conv2D(filters=
|
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+
x = Conv2D(filters=8, **params)(x)
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22
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x = MaxPool2D(pool_size=(2,2), strides=None, padding='same')(x)
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23
31
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24
32
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x = Flatten()(x)
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x = concatenate([x, append_input])
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34
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x = Dropout(0.5)(x)
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27
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-
x = Dense(128, activation='elu')(x)
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35
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+
x = Dense(128, activation='swish', kernel_initializer = 'he_uniform')(x)
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28
36
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x = Dense(10, activation='softmax')(x)
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29
37
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network = Model(inputs = [image_input, append_input], outputs = x)
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39
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32
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-
network.compile(optimizer='
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+
network.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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33
41
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network.summary()
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from keras.utils.vis_utils import plot_model
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@@ -37,17 +45,21 @@
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37
45
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38
46
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train_img = np.random.randn(32, 256, 128, 1)
|
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39
47
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train_feature = np.random.randn(32, 5, 1)
|
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40
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-
train_y = np.random.randn(32, 10)
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48
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+
train_y = np.abs(np.random.randn(32, 10))
|
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41
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batch_size = 32
|
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42
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epochs = 10
|
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43
51
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|
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44
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network.fit(
|
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53
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+
x = { # レイヤの名前と一致させる
|
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54
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+
"image_input": train_img,
|
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45
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-
|
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55
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+
"feature_input": train_feature
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+
},
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46
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y = train_y,
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47
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batch_size = batch_size,
|
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48
59
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epochs = epochs
|
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49
60
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)
|
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50
61
|
```
|
|
62
|
+
基本，分類問題を解かせる場合はTrainable paramsは出力側に行くにつれ減少させるのがセオリーです．カーネルフィルタの枚数を徐々に減少させるようにしておきました．また，活性化関数もELUを上回ったSwishを推奨しておきます．さらに，カーネルフィルタの初期値は`glorot_uniform`なので，ELUやSwishのようなReLUファミリ用の`he_uniform`を推奨します．
|
|
51
|
-
|
|
52
64
|
|
|
53
65
|
個人的には，CNNだけを分類予測学習した後，CNNを学習しないようにして特徴量マップ出力と追加情報を合併して予測/分類する方が良いと思います．
|
1
fix code
test
CHANGED
|
@@ -5,6 +5,7 @@
|
|
|
5
5
|
from keras.models import Model
|
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6
6
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from keras.layers import Conv2D, Input, Flatten, Dense, MaxPool2D, Dropout, concatenate
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7
7
|
from keras.optimizers import Adam
|
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8
|
+
import numpy as np
|
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8
9
|
|
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9
10
|
image_input = Input(shape = (256, 128, 1), name = "image_input")
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10
11
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append_input = Input(shape = (5), name = "feature_input") # 追加情報の特徴量の数だけ
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@@ -32,11 +33,17 @@
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32
33
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network.summary()
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34
35
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from keras.utils.vis_utils import plot_model
|
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35
|
-
plot_model(network, to_file = "CNN.png", show_shapes = True, show_layer_names = True, show_layer_activations = True)
|
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36
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+
plot_model(network, to_file = "CNN.png", rankdir = "LR", show_shapes = True, show_layer_names = True, show_layer_activations = True)
|
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37
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+
|
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38
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+
train_img = np.random.randn(32, 256, 128, 1)
|
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39
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+
train_feature = np.random.randn(32, 5, 1)
|
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40
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+
train_y = np.random.randn(32, 10)
|
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41
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+
batch_size = 32
|
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42
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+
epochs = 10
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36
43
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37
44
|
network.fit(
|
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38
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-
x =
|
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45
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+
x = {"image_input": train_img, "feature_input": train_feature},
|
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39
|
-
y = tri
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46
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+
y = train_y,
|
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40
47
|
batch_size = batch_size,
|
|
41
48
|
epochs = epochs
|
|
42
49
|
)
|