tensorflowとKerasを使ってTextClassificationをしたい

– Neural network model

> We have to multiply each input node by a weight w and add a bias b.
> It is generally common to use a rectified linear unit (ReLU) for hidden layers, a sigmoid function for the output layer in a binary classification problem, or a softmax function for the output layer of multi-class classification problems.

### Keras
– Keras is a deep learning and neural networks API by Francois Chollet
$ pip3 install keras

kerasを使うにはbackgroundにtensorflowが動いていないといけないので、amazon linux2にtensorflowをインストールします。
$ pip3 install tensorflow
$ python3 -c “import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))”
tf.Tensor(-784.01, shape=(), dtype=float32)
上手くインストールできたようです。

from keras.models import Sequential
from keras import layers

// 省略
input_dim = X_train.shape[1]

model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

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

$ python3 split.py
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 10) 17150
_________________________________________________________________
dense_1 (Dense) (None, 1) 11
=================================================================
Total params: 17,161
Trainable params: 17,161
Non-trainable params: 0
_________________________________________________________________
None

### batch size

history = model.fit(X_train, y_train,
					epochs=100,
					verbose=False,
					validation_data=(X_test, y_test)
					batch_size=10)

### evaluate accuracy

loss, accuracy = model.evaluate(X_train, y_train, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))
loss, accuracy = model.evaluate(X_test, y_test, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))

$ python3 split.py
Training Accuracy: 1.0000
Training Accuracy: 0.8040

### matplotlib
$ pip3 install matplotlib

import matplotlib.pyplot as plt

// 省略
def plot_history(history):
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    x = range(1, len(acc) + 1)

    plt.figure(figsize=(12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(x, acc, 'b', label='Training acc')
    plt.plot(x, val_acc, 'r', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.legend()
    plt.subplot(1, 2, 2)
    plt.plot(x, loss, 'b', label='Training loss')
    plt.plot(x, val_loss, 'r', label='Validation loss')
    plt.title('Training and validation loss')
    plt.savefig("img.png")

plot_history(history)

おおお、なんか凄え