機械学習って、すごいですね。

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
from keras.callbacks import EarlyStopping
from sklearn.model_selection import train_test_split

先日の手書きデータです。

x_train = np.array([ 
[0,0,0,0,1,0,0,1,1,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0],
[0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0],
[0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0],
[0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0,1,1,0,0,0],
[0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,0,0,0,0],
[0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,0,0,0],
[0,0,0,0,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0],
[0,0,0,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0],
[0,0,0,0,0,0,0,1,1,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0],
[0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0,0,1,0,0,0],
[0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,0,0,0,1,0,0,0,0],
[0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0],

[0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,1],
[1,1,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,1,1],
[1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0],
[0,0,0,0,0,1,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,1,0,0,0,0,1,1],
[1,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,1,0],
[0,0,0,0,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0],
[0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0],
[0,0,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,1],
[0,1,0,0,0,0,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,0],
[0,1,0,0,0,0,1,1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,0],
[1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1],  
])

右上から左下への線は「1」
左上から右下への線は「0」としようと思います。

y_train = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,])
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=0)
print(x_train.shape, x_val.shape, y_train.shape, y_val.shape)

(24, 25) (6, 25) (24,) (6,)

model = models.Sequential()
model.add(layers.Dense(1, activation='sigmoid', input_shape=(25,)))
model.summary()

Model: "sequential_13"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_27 (Dense) (None, 1) 26
=================================================================
Total params: 26
Trainable params: 26
Non-trainable params: 0
_________________________________________________________________

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

100回くらいでしたら、いまいちでしたので、
正しいのか分かりませんが、1000回で。

history = model.fit(x=x_train, 
                    y=y_train, 
                    epochs=1000, 
                    verbose=0, 
                    validation_data=(x_val, y_val))

history_dict = history.history
acc = history_dict['accuracy']
val_acc =  history_dict['val_accuracy']
epochs = range(1, len(acc) + 1)

plt.figure(figsize=(12, 8))
plt.plot(epochs, acc, label='acc')
plt.plot(epochs, val_acc, label='val_acc')
plt.ylim((0, 1))
plt.legend(loc='best')
plt.show()

できたモデルに、追加データで予測。

test = np.array([
[0,0,0,0,1,0,0,0,0,1,0,0,1,1,1,0,1,1,0,0,0,1,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,1,1,1,0,0],
[0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0],
[0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,1,0,0],
[1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0],
[0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,1,0],
[0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,1,1,1],
[0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,1,1,1]])

やばい、すご過ぎる。

predictions = model.predict(test)
print(predictions)

[[0.92442065]
[0.90009964]
[0.9115938 ]
[0.9031062 ]
[0.38198504]
[0.12939417]
[0.00990942]
[0.06089722]
[0.03855555]
[0.05584331]]

これが重みか〜。手動で計算できるかな？
確かに右上から左下にかけて、
大きな値になってるなー。すご。

print(len(l0.get_weights()))
print(l0.get_weights()[0])
print(l0.get_weights()[1])

2
[[-0.9030642 ]
[-0.58427167]
[-0.9232676 ]
[ 0.93159467]
[ 0.9419235 ]
[-0.50364554]
[-0.38859457]
[ 0.14480409]
[ 1.0228486 ]
[ 0.39397684]
[-0.27923834]
[-0.7368595 ]
[-1.126273 ]
[ 0.32510123]
[-0.38320494]
[ 0.98702824]
[ 0.7094111 ]
[ 0.9439723 ]
[-0.5872988 ]
[-0.4665192 ]
[ 0.60151327]
[ 0.5994672 ]
[ 0.59917206]
[-1.0496505 ]
[-0.46676916]]
[0.09961005]