In [1]:
from __future__ import print_function
import numpy as np
np.set_printoptions(threshold=np.nan) # NumPy:print時に要素を省略しない
np.random.seed(20170712) # NumPy:シード値を固定
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.models import load_model
import matplotlib.pyplot as plt
import sys
batch_size = 128 # 学習時のバッチ処理サイズ
num_classes = 10 # 0～9までの10分類
epochs = 1 # 学習回数
# 入力画像サイズ
img_rows, img_cols = 28, 28
# MNIST データセットのロード
# 0～9までの数字を手書きしたサイズ28x28のグレースケール画像データと、正解データ(描いた数字)のデータセット
# 60,000件の学習用データセットと10,000件の検証用データセットにわかれる
# (http://yann.lecun.com/exdb/mnist/)
#
# *_train : 学習用データ60,000件
# *_test : 検証用データ10,000件
# x_*** : 画像データ(サイズ28x28)
# y_*** : 正解データ(画像が描いた数字)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# テスト対象画像インデックス
eval_image_idx = 1
print('static unsigned int test_data[784] = {')
for row in range(28):
for col in range(28):
sys.stdout.write('%3d,' % x_test[eval_image_idx][row][col])
sys.stdout.write('\n')
print('};')
Using TensorFlow backend.
static unsigned int test_data[784] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,116,125,171,255,255,150, 93, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,169,253,253,253,253,253,253,218, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,169,253,253,253,213,142,176,253,253,122, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 52,250,253,210, 32, 12, 0, 6,206,253,140, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 77,251,210, 25, 0, 0, 0,122,248,253, 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 31, 18, 0, 0, 0, 0,209,253,253, 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,117,247,253,198, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 76,247,253,231, 63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,128,253,253,144, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,176,246,253,159, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25,234,253,233, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,198,253,253,141, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 78,248,253,189, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 19,200,253,253,141, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,134,253,253,173, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,248,253,253, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,248,253,253, 43, 20, 20, 20, 20, 5, 0, 5, 20, 20, 37,150,150,150,147, 10, 0,
0, 0, 0, 0, 0, 0, 0, 0,248,253,253,253,253,253,253,253,168,143,166,253,253,253,253,253,253,253,123, 0,
0, 0, 0, 0, 0, 0, 0, 0,174,253,253,253,253,253,253,253,253,253,253,253,249,247,247,169,117,117, 57, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,118,123,123,123,166,253,253,253,155,123,123, 41, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
};
In [180]:
# 画像データの値を256階調グレースケールから0.0～1.0に変換
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# 正解データを0～9の数字からone-hot表現の1次元配列に変換
# 0 -> [1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# 1 -> [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
# :
# 9 -> [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 画像データのイメージを表示
plt.imshow(x_test[eval_image_idx])
plt.gray()
plt.show()
images = x_test.reshape(10000, 28, 28)
print('const unsigned int test_data[784] = {')
for row in range(28):
for col in range(28):
sys.stdout.write('%f,' % images[eval_image_idx][row][col])
sys.stdout.write('\n')
print('};')
# 正解データを表示
print(y_test[eval_image_idx])
x_eval = images[eval_image_idx].reshape(1, 28, 28, 1)
y_eval = y_test[eval_image_idx].reshape(1, 10)
# コンパイル済み、学習済みモデルをロード
model_test = load_model('./CQseminar/mnist_cnn_train1_fine20_fine100.h5')
# 検証
score = model_test.evaluate(x_eval, y_eval, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# predict
ret = model_test.predict(x_eval, 1, 1)
print(ret)
x_train shape: (60000, 28, 28)
60000 train samples
10000 test samples
const unsigned int test_data[784] = {
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.454902,0.490196,0.670588,1.000000,1.000000,0.588235,0.364706,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.662745,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.854902,0.117647,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.662745,0.992157,0.992157,0.992157,0.835294,0.556863,0.690196,0.992157,0.992157,0.478431,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.203922,0.980392,0.992157,0.823529,0.125490,0.047059,0.000000,0.023529,0.807843,0.992157,0.549020,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.301961,0.984314,0.823529,0.098039,0.000000,0.000000,0.000000,0.478431,0.972549,0.992157,0.254902,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.121569,0.070588,0.000000,0.000000,0.000000,0.000000,0.819608,0.992157,0.992157,0.254902,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.458824,0.968627,0.992157,0.776471,0.039216,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.298039,0.968627,0.992157,0.905882,0.247059,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.501961,0.992157,0.992157,0.564706,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.690196,0.964706,0.992157,0.623529,0.047059,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.098039,0.917647,0.992157,0.913725,0.137255,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.776471,0.992157,0.992157,0.552941,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.305882,0.972549,0.992157,0.741176,0.047059,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.074510,0.784314,0.992157,0.992157,0.552941,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.525490,0.992157,0.992157,0.678431,0.047059,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.972549,0.992157,0.992157,0.098039,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.972549,0.992157,0.992157,0.168627,0.078431,0.078431,0.078431,0.078431,0.019608,0.000000,0.019608,0.078431,0.078431,0.145098,0.588235,0.588235,0.588235,0.576471,0.039216,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.972549,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.658824,0.560784,0.650980,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.482353,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.682353,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.992157,0.976471,0.968627,0.968627,0.662745,0.458824,0.458824,0.223529,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.462745,0.482353,0.482353,0.482353,0.650980,0.992157,0.992157,0.992157,0.607843,0.482353,0.482353,0.160784,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,
};
[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
Test loss: 1.19209289551e-07
Test accuracy: 1.0
1/1 [==============================] - 0s
[[ 6.42633177e-17 7.80472198e-16 1.00000000e+00 2.94985066e-19
1.48914927e-22 6.86327605e-24 2.98993906e-15 5.05373026e-19
3.42325425e-21 4.62921721e-28]]
In [181]:
print(x_eval[0])
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[ 0.99215686]
[ 0.99215686]
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[ 0.6901961 ]
[ 0.99215686]
[ 0.99215686]
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[ 0.20392157]
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In [182]:
# 検証用：中間層（隠れ層）の出力
#
# Keras FAQ: 中間レイヤーの出力を得るには？
# https://keras.io/ja/getting-started/faq/#_1
#
# 畳み込み層 5x5x1x16 ReLU
layidx = 0;
get_intermediate_layer_output = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = get_intermediate_layer_output([x_eval,])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[0] output
(1, 24, 24, 16)
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In [183]:
params = model_test.layers[layidx].get_weights()
print("lay[%d] biases" % layidx)
print(params[1].shape)
print(params[1])
print("\n")
print("lay[%d] weights" % layidx)
print(params[0].shape)
print(params[0])
lay[0] biases
(16,)
[-0.62901038 -0.03752879 -0.29460651 -0.15698859 -0.24385601 -0.10194576
-0.11998731 -0.1383121 -0.0717945 -0.14473793 -0.4525702 -0.21036877
-0.04035919 0.18188125 -0.26269314 -0.32092035]
lay[0] weights
(5, 5, 1, 16)
[[[[ 0.18378262 -0.18095377 -0.15333341 0.02327174 0.05894852 -0.09210721
0.10724252 -0.12814869 -0.03665357 -0.00167961 0.01650576 -0.03069592
0.06378142 0.33862934 0.02058219 0.27421632]]
[[ 0.19155328 -0.04227424 -0.07556787 0.03851679 -0.12868293 -0.31772339
0.15001608 -0.20832637 0.05342739 0.24149352 0.06785257 -0.14032385
0.22037426 0.07897536 -0.00770759 -0.16798806]]
[[ 0.17629692 -0.22380014 0.11443559 0.26027137 -0.1908396 -0.15421279
0.30015585 0.28877619 -0.05625462 -0.02651599 0.27613828 -0.1071356
-0.05557648 -0.16797729 0.33488438 -0.58016545]]
[[ 0.16263162 -0.15671414 0.17128813 0.08102502 -0.20960581 -0.17067516
0.23822626 0.14689504 -0.19351801 -0.2212853 0.30102652 0.29138023
-0.13547294 0.08497631 0.25077668 -0.15406407]]
[[-0.01026443 0.01002361 0.14400584 0.0393662 -0.55967224 -0.00668133
0.28837225 0.21184643 -0.22742423 -0.17898728 -0.14989975 0.06514613
-0.36150444 0.20734312 0.12429916 -0.09759056]]]
[[[ 0.15567406 -0.13913605 -0.20291555 0.26030159 -0.1842681 -0.22818208
-0.2880041 -0.36756945 -0.04928368 0.02222761 -0.02507421 0.20749211
0.12138005 -0.01049831 0.10628239 -0.02748027]]
[[-0.0027497 -0.42789999 0.10820925 0.05199576 0.1210124 -0.14493445
-0.17957988 -0.11643492 0.22491187 -0.11099664 0.00456836 -0.23631921
0.14817509 -0.0996023 -0.07907257 -0.20414679]]
[[-0.3828682 -0.21571791 0.26666966 0.03045612 0.26806682 0.17157161
0.10877197 0.12532064 0.08181296 -0.44801226 -0.20265639 -0.51043087
-0.08753796 0.09852006 -0.02850566 0.20454195]]
[[-0.08421923 -0.04900339 0.09351327 0.09867585 0.29791832 -0.03363731
-0.0327458 -0.34299406 0.05492518 -0.18807523 0.22893082 0.05247142
-0.16822016 0.12496826 0.05597453 0.46500933]]
[[-0.22640312 0.18723394 -0.19285616 -0.19592068 -0.36677101 -0.01624428
-0.05043334 -0.17765433 -0.00498492 0.02469731 0.11097076 0.38130611
-0.19323359 -0.11088228 0.16125199 0.13469113]]]
[[[ 0.02576087 -0.40211156 0.16100486 0.01175185 0.28229287 -0.30051112
-0.18817182 -0.40597421 -0.14014402 -0.02748445 -0.20742849 0.33742985
0.25053829 -0.24608377 -0.13590167 0.16523601]]
[[-0.27592453 -0.01917703 0.23938498 -0.0909076 0.10455287 0.27125746
-0.51904196 -0.03185857 0.13900802 -0.29796311 -0.09136382 -0.12182511
0.08624748 0.18551414 -0.14150196 0.13796763]]
[[-0.24134773 -0.07178742 0.06262599 0.22692436 0.06688057 0.26798493
-0.6701501 0.5466345 0.24921894 0.13462898 -0.23633495 -0.60175163
-0.01655059 0.15575564 -0.0234334 0.39248618]]
[[ 0.33348781 0.18587811 -0.21527208 0.11596174 0.36605883 -0.20255361
-0.40750456 0.03067493 0.37116823 0.40874079 0.21805903 -0.23985961
-0.29206353 0.01013208 0.08161234 -0.17740269]]
[[-0.06140964 0.21087477 -0.16747704 -0.42543387 0.07875653 -0.25847441
-0.11262386 -0.10268337 0.04426527 0.3302637 -0.07118786 0.2038728
-0.31989586 -0.36547849 0.15172489 -0.13877942]]]
[[[ 0.20179951 -0.61912906 0.08825527 -0.11697931 -0.1206877 -0.22258157
0.20547369 -0.55813062 -0.28330126 0.08103431 -0.2332488 0.20181656
0.07267697 -0.23192695 -0.08164368 0.09779516]]
[[ 0.18244265 0.06674994 0.00806855 0.17336126 -0.33228794 0.41136599
0.0648536 -0.35042733 -0.38456511 0.12581097 -0.13635273 0.40350923
0.10451807 -0.21997863 -0.39693493 -0.03941998]]
[[ 0.15844405 0.07281931 -0.43501303 0.22707686 -0.49739617 -0.31085503
-0.11521836 0.36016876 0.02367935 0.26757002 -0.02336688 -0.32208598
-0.123024 -0.25707659 -0.25692251 -0.1237626 ]]
[[ 0.39778355 0.14360364 -0.52666283 -0.45918056 -0.18441132 -0.3952364
-0.22484234 -0.01827278 -0.03909436 0.01445972 0.35286808 -0.26481202
-0.06712179 -0.92920792 -0.19413809 -0.29800254]]
[[-0.03897737 0.0330917 0.20215686 -0.17829484 0.17865258 -0.06381495
-0.17240556 -0.03438797 0.17279893 -0.14129347 -0.10305486 0.04227751
-0.08916236 -0.69718176 0.06292085 -0.12205695]]]
[[[-0.00723472 -0.20257908 0.11045035 -0.44888207 0.21815221 -0.06657762
0.14234903 -0.03018231 0.05469951 0.0616139 0.03846187 0.0096822
-0.0454447 -1.02260101 -0.16356716 0.07821075]]
[[ 0.23418909 0.06894027 -0.10716635 -0.12270977 -0.02892148 0.44014463
0.20548481 -0.69548142 -0.41104418 0.12856574 -0.00695143 0.26230168
-0.06526937 -1.11847198 -0.07712324 -0.26734155]]
[[-0.0076174 -0.00410234 -0.06264415 -0.4097085 0.24645633 0.02551057
0.2596544 -0.19092694 -0.36084244 -0.22664325 0.17729799 0.21552478
-0.18094584 -1.05718529 -0.27964291 0.00241093]]
[[-0.23647863 0.10428108 0.19797413 -0.13874678 -0.11519748 -0.0975792
0.24085084 0.01596858 -0.2503368 -0.12705797 0.25577614 -0.03380172
0.05143771 0.0093581 -0.05249907 0.14982545]]
[[-0.42448908 0.01440317 0.25296333 0.08752578 -0.01594228 0.15540047
0.14800322 0.08607971 -0.09098312 -0.0211668 -0.24563804 -0.11571542
-0.06416921 0.00456992 0.13190754 0.24916038]]]]
In [184]:
# MAXプーリング層 2x2
layidx = 1;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[1] output
(1, 12, 12, 16)
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In [185]:
# 畳み込み層 5x5x32 ReLU
layidx = 2;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[2] output
(1, 8, 8, 32)
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In [186]:
params = model_test.layers[layidx].get_weights()
print("lay[%d] biases" % layidx)
print(params[1].shape)
print(params[1])
print("\n")
print("lay[%d] weights" % layidx)
print(params[0].shape)
print(params[0])
lay[2] biases
(32,)
[-0.06688104 -0.1983033 -0.14112106 -0.19895078 -0.11946137 -0.00138727
-0.10215919 -0.18809502 -0.16000935 -0.13021451 -0.26266673 -0.24011953
-0.0157583 -0.07577139 -0.02844524 -0.15672483 -0.08910633 0.04451979
-0.27768332 -0.25149834 -0.13130423 -0.2036982 -0.37708294 -0.28505778
-0.15767874 -0.16174936 -0.11188391 -0.30844539 -0.06414489 -0.29297864
-0.13430218 -0.11170881]
lay[2] weights
(5, 5, 16, 32)
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-1.25934556e-01 -3.81777912e-01 -7.78286383e-02 -1.83577582e-01
3.22976232e-01 2.01609507e-01 -1.24656938e-01 -2.49040842e-01]
[ 1.29153684e-01 7.19984844e-02 -3.14399600e-02 -1.13711439e-01
1.89576566e-01 2.57124156e-01 8.51863772e-02 -1.61436185e-01
1.74047872e-01 1.77983552e-01 2.88877428e-01 -1.39514536e-01
-1.44656286e-01 3.61273834e-03 4.67779860e-02 1.48060769e-01
-2.17850700e-01 -1.81423649e-02 9.97116715e-02 1.71345860e-01
-6.22339666e-01 -3.11568882e-02 4.98796225e-01 4.44430336e-02
-3.11337002e-02 -2.98734605e-01 1.65781051e-01 -3.43628645e-01
-4.04255092e-03 1.86183438e-01 -3.62270534e-01 -6.79210603e-01]
[ 2.23814130e-01 -2.77065754e-01 -1.20198719e-01 2.24090174e-01
3.48993033e-01 -1.11525441e-02 -2.11283818e-01 -1.39725640e-01
-2.58983055e-04 -2.36185849e-01 1.54997528e-01 2.62650270e-02
1.47525176e-01 4.52816844e-01 -3.39200586e-01 4.94251400e-02
-4.37350631e-01 -1.41774490e-01 -2.42621124e-01 -2.55348116e-01
4.32206422e-01 -6.80258274e-01 8.13379884e-03 1.98175147e-01
-3.38601619e-02 -9.16147754e-02 -1.74439684e-01 1.30096301e-01
-1.21488206e-01 -2.78251320e-02 1.29406825e-01 1.54800877e-01]
[ 2.18112692e-01 5.14888167e-02 2.69323587e-01 2.50733912e-01
5.34887947e-02 1.24616042e-01 3.01686645e-01 3.10740530e-01
2.96460539e-01 2.92698473e-01 -1.26979113e-01 -1.75795212e-01
1.69637531e-01 3.81690085e-01 -4.40970689e-01 8.78660604e-02
-2.22390696e-01 -6.38767204e-04 1.15704291e-01 -3.94018739e-02
-9.98999253e-02 -4.86110926e-01 -1.04286961e-01 4.56894152e-02
3.52898464e-02 8.70096684e-02 1.98260561e-01 1.26726598e-01
4.12574619e-01 3.44672762e-02 -1.37888372e-01 2.51160245e-02]
[ 4.13676977e-01 6.62867203e-02 -8.04378390e-02 2.52828211e-01
-2.53278464e-01 1.58834875e-01 1.19914733e-01 5.37889004e-01
-3.19538951e-01 -1.56951487e-01 -5.40161967e-01 -2.32290953e-01
-1.29963011e-01 -2.90015787e-01 4.08212505e-02 1.29423812e-01
-3.94891888e-01 -4.37167555e-01 -2.67825037e-01 -2.42041662e-01
1.61498234e-01 2.66142875e-01 2.88593948e-01 -1.66492820e-01
1.90388992e-01 -1.20991819e-01 1.37045458e-01 -3.29320915e-02
7.00242370e-02 1.92131102e-01 2.62020499e-01 4.12013263e-01]
[ -1.57615036e-01 -5.89039445e-01 6.43833727e-02 2.12727576e-01
-3.87401462e-01 -3.39150339e-01 -1.04355909e-01 -1.91226080e-01
-1.75749362e-01 8.97388607e-02 -8.28803480e-01 1.93813905e-01
-2.29856685e-01 1.18192770e-01 2.97446340e-01 -1.14587964e-02
-4.86556351e-01 3.77929807e-01 -4.53653574e-01 1.10943772e-01
-4.29336160e-01 -4.38759685e-01 -2.20016867e-01 -2.61148632e-01
5.52857574e-03 -4.70905095e-01 1.48498282e-01 4.06437397e-01
1.28259718e-01 -2.90757895e-01 -5.16017005e-02 1.80338755e-01]
[ -8.76430511e-01 -2.02885374e-01 1.84459612e-01 2.96882451e-01
-2.91471882e-03 -4.37450469e-01 -2.70876318e-01 -2.31338039e-01
2.87373036e-01 -1.07406802e-01 1.34588137e-01 -4.93459441e-02
-2.66407669e-01 -1.98281668e-02 -7.24958926e-02 2.52710640e-01
7.88503736e-02 -8.65358338e-02 -7.01397583e-02 -8.98928717e-02
1.15714781e-01 -2.82898575e-01 2.96434432e-01 1.84806719e-01
1.42665148e-01 -3.09498250e-01 -1.09806880e-01 -2.95269430e-01
3.20141703e-01 2.38173366e-01 1.04139403e-01 3.79407883e-01]
[ 2.19477251e-01 -3.59603427e-02 7.55129606e-02 -3.87753934e-01
1.21644020e-01 1.45981878e-01 4.92984541e-02 -8.40950087e-02
-1.98687911e-01 -3.08051348e-01 2.69879103e-01 -1.14069827e-01
1.33607656e-01 -3.97406995e-01 -7.63854742e-01 1.37422919e-01
2.22055674e-01 -1.83273152e-01 -5.21512404e-02 -2.69864917e-01
-2.26108536e-01 -1.03215945e+00 2.20724463e-01 1.34675100e-01
1.22782841e-01 -1.97495237e-01 4.25769985e-01 -2.67231584e-01
5.19199729e-01 -1.65889218e-01 -1.13000579e-01 2.06893876e-01]
[ -5.75199910e-02 4.60939854e-02 2.67765492e-01 -3.54290843e-01
-5.49179673e-01 -9.39746678e-04 -1.73113093e-01 -9.09596756e-02
1.48061812e-02 2.26452813e-01 -2.91338295e-01 -4.59602565e-01
-2.43189946e-01 -1.58251915e-02 -2.07961723e-01 -2.05487683e-01
-4.11654115e-01 -3.32708985e-01 -3.93334419e-01 4.16240841e-02
1.23133473e-01 -3.91697407e-01 1.40270591e-02 -4.61572498e-01
-1.06133707e-01 9.40679945e-03 -7.11641982e-02 3.51487905e-01
-2.40498617e-01 -5.08370697e-01 -2.83120006e-01 -4.62670445e-01]
[ 1.17434852e-01 -1.19111396e-01 -1.67588711e-01 1.01921603e-01
-2.27865607e-01 2.63569355e-01 3.35408270e-01 2.27897063e-01
-4.09768164e-01 2.23607495e-01 -3.05694759e-01 1.21683739e-01
9.61847156e-02 -3.81074250e-01 -1.21274218e-01 -3.62467647e-01
-2.34229535e-01 -2.43912488e-01 -1.55520618e-01 1.84600800e-01
1.27789289e-01 -6.25654161e-02 2.95050740e-01 -6.60525337e-02
2.12738574e-01 -1.39189512e-02 -3.70536074e-02 -2.03888088e-01
-1.52560204e-01 -1.75316453e-01 -3.10994178e-01 -2.91946400e-02]
[ 1.16726927e-01 1.90696448e-01 1.09674647e-01 -1.02210427e-02
1.62337482e-01 -4.21363376e-02 9.44840983e-02 8.12741667e-02
6.74917549e-02 -3.41244638e-02 2.28347182e-01 5.85307777e-02
-3.55729759e-02 2.55034361e-02 4.71905284e-02 -1.95658788e-01
-4.84308787e-02 1.99651256e-01 3.35398838e-02 6.26483709e-02
6.64016083e-02 -1.10172406e-01 1.27885818e-01 1.80363432e-01
-9.90640223e-02 6.94233626e-02 -8.99891742e-03 -5.16002029e-02
-3.09829026e-01 -1.84974372e-01 -1.62650302e-01 -1.16088904e-01]
[ -7.12133721e-02 1.50784299e-01 -2.32308328e-01 1.53166950e-01
3.95033099e-02 8.71044677e-03 1.45349607e-01 -5.65095879e-02
-3.22200470e-02 -2.25087330e-01 -3.68369110e-02 -1.37674361e-01
-3.07883143e-01 -5.00444435e-02 -8.84430036e-02 3.19019794e-01
2.29741812e-01 1.67132139e-01 -1.55339809e-02 1.35675535e-01
-2.14628428e-01 1.20084077e-01 3.26033741e-01 3.35278399e-02
2.04520762e-01 -1.38006091e-01 1.18009791e-01 -2.91669250e-01
8.19559116e-03 -7.91711956e-02 2.83656925e-01 -2.45650098e-01]
[ -6.57310903e-01 1.29941091e-01 5.33322841e-02 -1.40798837e-01
-4.75919172e-02 -1.46889001e-01 -2.21278489e-01 -2.56148577e-01
-1.58272088e-02 -1.41103551e-01 -1.16194628e-01 -3.00016761e-01
-1.12262733e-01 6.12417161e-02 1.77572697e-01 2.13995069e-01
1.89698506e-02 -2.00022370e-01 -7.61078745e-02 2.67135557e-02
-3.32732618e-01 -5.48346937e-01 1.96666554e-01 -9.36033726e-02
9.50818881e-02 -5.37109077e-01 -7.94996694e-02 -5.88298477e-02
1.64664105e-01 7.89825693e-02 7.46717453e-02 -3.85772765e-01]
[ 1.64946660e-01 -8.23747218e-02 -2.88022518e-01 -2.81194270e-01
-1.30406648e-01 1.07147895e-01 1.14976123e-01 -2.19038218e-01
-1.35878354e-01 1.31796515e-02 9.39497575e-02 2.15559393e-01
-1.75964117e-01 -2.81619746e-02 -4.93681043e-01 -1.29367635e-01
1.42930552e-01 1.00709619e-02 1.99885309e-01 1.70552060e-01
2.14792475e-01 -3.16079199e-01 1.93630289e-02 2.93511838e-01
1.29672870e-01 -5.08902311e-01 1.21570170e-01 -1.70681983e-01
2.91375250e-01 -2.66669601e-01 1.35719419e-01 1.47899792e-01]]]]
In [187]:
# MAXプーリング層 2x2
layidx = 3;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[3] output
(1, 4, 4, 32)
[array([[[[ 0. , 0. , 0. , 0.57450354, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.22666998, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.01115561, 0. , 0. , 0.44901836, 0. ,
0. , 0. , 0. , 0. , 0. ,
1.09769988, 1.99758756],
[ 0. , 0. , 0. , 2.37766743, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.83379334, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.24161911, 0. , 0. , 0. , 1.43549526,
0.10232703, 0. ],
[ 0. , 0. , 0. , 0.08730023, 0. ,
0. , 0.38791269, 0. , 0. , 0.49400491,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1.50689936, 1.04254627, 0. , 0.55364633,
0. , 0. ],
[ 0. , 0. , 0. , 0.13233255, 0.4515118 ,
0. , 0. , 0.63079029, 0. , 0. ,
0. , 0. , 0.8903302 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.71329844, 0.29695773, 0. , 0. ,
0. , 0. ]],
[[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.69050252, 0. , 0. ,
0. , 0. , 0. , 0.10155332, 0. ,
0. , 0.52657056, 0.39786309, 0. , 0. ,
0.80714816, 0. ],
[ 0. , 1.01646972, 0. , 0. , 0. ,
0.64961123, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.64949095,
0. , 0. , 0.88736296, 0. , 0.84021497,
0.48272189, 0. , 0.07269505, 0. , 0. ,
0.69914126, 0. , 0. , 0.52435803, 0. ,
0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ,
0.16376647, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1.02804875,
0.5406462 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0.01454882, 0. , 0. , 0. ,
0. , 0. , 0.30691272, 0. , 0. ,
0. , 0. , 0.68204623, 0. , 0. ,
0. , 0. , 0.84107327, 0. , 0. ,
0.57197046, 0. , 0. , 0. , 0. ,
0.4829154 , 0. , 0. , 0. , 0. ,
0. , 0. ]],
[[ 0.66077256, 0. , 0. , 0.03763244, 0. ,
0.13187099, 0. , 0. , 0.39785799, 0. ,
0. , 0. , 0. , 0. , 0.18827158,
0. , 0. , 0.46316782, 0. , 0. ,
0. , 0. , 0.37721765, 0. , 0.05231114,
0.26543114, 0. , 0. , 2.17484021, 0. ,
0. , 0. ],
[ 0.95305145, 0. , 0. , 0. , 0. ,
1.12575853, 0. , 0. , 1.41320062, 0. ,
2.35952544, 0. , 0. , 0. , 0.04591295,
0. , 0. , 0. , 0. , 0.57951111,
0.69792163, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.39157152, 0. ,
0. , 0. ],
[ 0. , 0.12334923, 0.64388943, 0. , 0. ,
0. , 0. , 0. , 1.95578003, 0. ,
1.9453299 , 0.18984191, 0. , 0. , 0. ,
0. , 0.35417956, 0. , 0.0909273 , 0. ,
1.38885391, 0. , 0.43438268, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0.26586974, 0. , 0. , 0. ,
0. , 0. , 0.75423461, 0.64080906, 0. ,
1.99800539, 0.3073163 , 0. , 0. , 0. ,
0. , 0. , 0. , 0.69204766, 0. ,
1.02580225, 0. , 0. , 1.12587643, 0. ,
0.01806313, 0. , 0. , 0. , 0. ,
0. , 0. ]],
[[ 0. , 0. , 0. , 0. , 0.08562257,
0.00994977, 0. , 0.8348375 , 0. , 0. ,
0. , 0. , 0. , 1.33661795, 0.40280259,
1.19430506, 1.3474133 , 0. , 0. , 0. ,
0. , 0. , 0.48046345, 0. , 0.19381995,
0. , 0. , 0. , 0.61780363, 0. ,
0. , 0.8994388 ],
[ 0. , 0.71028024, 1.6086663 , 0. , 0. ,
0. , 0. , 0. , 0.73397732, 0. ,
1.81053686, 0. , 0. , 1.14013028, 0. ,
1.49976122, 0. , 0. , 0.61040848, 0. ,
0. , 1.26010323, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0.95660311, 1.85851848, 0. , 0. ,
0. , 0. , 0. , 0.03375022, 0. ,
1.06805253, 0.61311543, 0. , 1.87090731, 0.90102208,
1.37305737, 0. , 0. , 0.74129206, 0. ,
0. , 2.7356627 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.76226306, 0.83254308, 0. , 1.13983464, 1.66775119,
0. , 0. , 0. , 0. , 0. ,
0. , 2.34857368, 0. , 0.86482286, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.11140055]]]], dtype=float32)]
In [188]:
# 平滑化層
layidx = 5;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[5] output
(1, 512)
[array([[ 0. , 0. , 0. , 0.57450354, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.22666998, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.01115561, 0. , 0. , 0.44901836, 0. ,
0. , 0. , 0. , 0. , 0. ,
1.09769988, 1.99758756, 0. , 0. , 0. ,
2.37766743, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.83379334, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 1.24161911, 0. , 0. ,
0. , 1.43549526, 0.10232703, 0. , 0. ,
0. , 0. , 0.08730023, 0. , 0. ,
0.38791269, 0. , 0. , 0.49400491, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.50689936, 1.04254627, 0. , 0.55364633, 0. ,
0. , 0. , 0. , 0. , 0.13233255,
0.4515118 , 0. , 0. , 0.63079029, 0. ,
0. , 0. , 0. , 0.8903302 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.71329844, 0.29695773, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.69050252, 0. , 0. , 0. , 0. ,
0. , 0.10155332, 0. , 0. , 0.52657056,
0.39786309, 0. , 0. , 0.80714816, 0. ,
0. , 1.01646972, 0. , 0. , 0. ,
0.64961123, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.64949095,
0. , 0. , 0.88736296, 0. , 0.84021497,
0.48272189, 0. , 0.07269505, 0. , 0. ,
0.69914126, 0. , 0. , 0.52435803, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.16376647, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1.02804875, 0.5406462 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01454882, 0. , 0. , 0. , 0. ,
0. , 0.30691272, 0. , 0. , 0. ,
0. , 0.68204623, 0. , 0. , 0. ,
0. , 0.84107327, 0. , 0. , 0.57197046,
0. , 0. , 0. , 0. , 0.4829154 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.66077256, 0. , 0. , 0.03763244,
0. , 0.13187099, 0. , 0. , 0.39785799,
0. , 0. , 0. , 0. , 0. ,
0.18827158, 0. , 0. , 0.46316782, 0. ,
0. , 0. , 0. , 0.37721765, 0. ,
0.05231114, 0.26543114, 0. , 0. , 2.17484021,
0. , 0. , 0. , 0.95305145, 0. ,
0. , 0. , 0. , 1.12575853, 0. ,
0. , 1.41320062, 0. , 2.35952544, 0. ,
0. , 0. , 0.04591295, 0. , 0. ,
0. , 0. , 0.57951111, 0.69792163, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.39157152, 0. , 0. , 0. ,
0. , 0.12334923, 0.64388943, 0. , 0. ,
0. , 0. , 0. , 1.95578003, 0. ,
1.9453299 , 0.18984191, 0. , 0. , 0. ,
0. , 0.35417956, 0. , 0.0909273 , 0. ,
1.38885391, 0. , 0.43438268, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.26586974, 0. ,
0. , 0. , 0. , 0. , 0.75423461,
0.64080906, 0. , 1.99800539, 0.3073163 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.69204766, 0. , 1.02580225, 0. , 0. ,
1.12587643, 0. , 0.01806313, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.08562257, 0.00994977,
0. , 0.8348375 , 0. , 0. , 0. ,
0. , 0. , 1.33661795, 0.40280259, 1.19430506,
1.3474133 , 0. , 0. , 0. , 0. ,
0. , 0.48046345, 0. , 0.19381995, 0. ,
0. , 0. , 0.61780363, 0. , 0. ,
0.8994388 , 0. , 0.71028024, 1.6086663 , 0. ,
0. , 0. , 0. , 0. , 0.73397732,
0. , 1.81053686, 0. , 0. , 1.14013028,
0. , 1.49976122, 0. , 0. , 0.61040848,
0. , 0. , 1.26010323, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.95660311,
1.85851848, 0. , 0. , 0. , 0. ,
0. , 0.03375022, 0. , 1.06805253, 0.61311543,
0. , 1.87090731, 0.90102208, 1.37305737, 0. ,
0. , 0.74129206, 0. , 0. , 2.7356627 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.76226306, 0.83254308, 0. , 1.13983464, 1.66775119,
0. , 0. , 0. , 0. , 0. ,
0. , 2.34857368, 0. , 0.86482286, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.11140055]], dtype=float32)]
In [189]:
# 全結合層 ReLU
layidx = 6;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[6] output
(1, 128)
[array([[ 0. , 0. , 0. , 0. , 2.49670982,
0. , 0. , 0. , 0. , 7.22926188,
0. , 1.87134886, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 3.58523226, 0. , 0. ,
0.37005359, 3.88577771, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 3.53626823,
3.56130314, 0. , 0. , 0.98251677, 0. ,
0. , 1.24230266, 0. , 0. , 0. ,
0. , 0. , 6.23906994, 0. , 5.04799652,
6.26451826, 0. , 0. , 0. , 0. ,
5.29788065, 0. , 1.93103099, 5.15684938, 0. ,
0. , 2.15008044, 0. , 0. , 7.08297777,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.42690736, 0. , 0. , 0.99164611,
0. , 6.54911518, 3.84275126, 0. , 1.25991857,
0. , 0. , 0. , 0. , 0. ,
0. , 5.76325989, 0. , 0. , 3.28247309,
0. , 0. , 0. , 7.14583445, 0. ,
0. , 0.86342859, 0. , 0. , 6.16115093,
0. , 0.81246829, 4.44727898, 0. , 0. ,
3.80227971, 0. , 0. , 6.29695988, 0. ,
0.75093091, 0. , 0. , 0. , 3.13470459,
0. , 0. , 0. , 5.78036213, 3.04529023,
0. , 0. , 3.45044088]], dtype=float32)]
In [190]:
params = model_test.layers[layidx].get_weights()
print("lay[%d] biases" % layidx)
print(params[1].shape)
print(params[1])
print("\n")
print("lay[%d] weights" % layidx)
print(params[0].shape)
print(params[0])
lay[6] biases
(128,)
[ 0.04758736 0.11316533 -0.10801517 -0.1901128 -0.27275026 -0.05532031
-0.37535214 -0.07091651 -0.1634753 -0.33553544 0.00808175 -0.27438682
0.00656095 -0.17133953 -0.22275725 0.07776549 -0.31102592 -0.18464194
-0.28934637 -0.13687216 -0.01429875 -0.10413868 -0.32324374 -0.28888911
-0.15629324 -0.20690708 -0.24263081 -0.2826547 -0.06170291 -0.29486704
-0.141094 -0.29362756 -0.09399065 -0.18061267 -0.13005817 -0.24434449
-0.23079659 -0.03799224 -0.35820928 -0.12853096 0.00953153 -0.32465428
0.06038928 0.03488144 -0.36292526 -0.13677551 -0.12355311 -0.44612631
0.01236857 -0.05782548 -0.15379395 -0.11955843 -0.16403168 -0.25105527
-0.25413528 -0.25460783 0.16809143 -0.28238136 0.01332028 -0.10469628
-0.23232366 -0.11583672 -0.22051415 0.12021382 -0.43410882 -0.04025244
-0.17792226 -0.22668137 -0.05069969 -0.25100112 -0.27288753 -0.3949278
-0.28642485 -0.14651816 0.20377956 -0.31065291 -0.04065603 -0.38324106
0.08366539 -0.23287351 -0.25101364 -0.4550637 -0.29108983 -0.28115496
-0.30526385 -0.22191338 -0.1794354 -0.17959121 -0.28901076 -0.19239061
-0.21935375 -0.14659694 -0.18681173 0.02661786 -0.2393394 -0.18335491
-0.22204097 -0.00837635 -0.10929808 -0.33484346 -0.10565593 -0.17943993
-0.13668251 -0.15303704 -0.50283855 -0.11658236 -0.33628729 -0.01337868
-0.37841088 -0.27212 -0.30724874 0.08079609 -0.2158639 -0.55141091
-0.13176975 -0.21968137 -0.12433987 -0.03801202 -0.18287459 -0.25013563
-0.34616601 -0.07385798 0.03388635 -0.1074122 -0.43720421 -0.29990613
-0.25076392 0.02646121]
lay[6] weights
(512, 128)
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-4.47861791e-01 -3.68571758e-01 -5.70639297e-02 -8.83970559e-02
2.04805851e-01 2.99646795e-01 1.09092101e-01 3.35849226e-01]]
In [191]:
# 全結合層 Softmax
layidx = 8;
layer = K.function([model_test.layers[layidx].input], [model_test.layers[layidx].output])
layer_output = layer([layer_output[0],])
print("lay[%d] output" % layidx)
print(layer_output[0].shape)
print(layer_output)
lay[8] output
(1, 10)
[array([[ 6.42633177e-17, 7.80472198e-16, 1.00000000e+00,
2.94985066e-19, 1.48914927e-22, 6.86327605e-24,
2.98993906e-15, 5.05373026e-19, 3.42325425e-21,
4.62921721e-28]], dtype=float32)]
In [192]:
params = model_test.layers[layidx].get_weights()
print("lay[%d] biases" % layidx)
print(params[1].shape)
print(params[1])
print("\n")
print("lay[%d] weights" % layidx)
print(params[0].shape)
print(params[0])
lay[8] biases
(10,)
[ 0.44257981 0.77157462 0.06027411 -0.04234137 -0.36675683 -0.17116684
-0.49610204 0.09522778 -0.05281036 -0.10534358]
lay[8] weights
(128, 10)
[[ -4.24441189e-01 3.54200810e-01 1.02073401e-01 -1.47860467e-01
2.69033581e-01 -3.51690799e-01 -4.19047296e-01 -4.79509085e-01
-2.26208299e-01 -4.28662002e-01]
[ 1.52113438e-01 4.85386997e-01 -2.35885784e-01 -7.56868780e-01
-4.78294522e-01 1.42364595e-02 -3.99158806e-01 -1.99316144e-01
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Content source: RyujiTanaka/ds5_keras_mnist
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