################################
# 激活函數 + matplotlib圖
################################
import numpy as np
import matplotlib.pylab as plt
import tensorflow as tf

# 創建輸入數據
# (-7, 7)之間的等間隔的180個點
x = np.linspace(-7, 7, 180)


# 激動函數的原始實現
def sigmoid(inputs):
    y = [1 / float(1 + np.exp(-x)) for x in inputs]
    return y


def relu(inputs):
    y = [x * (x > 0) for x in inputs]
    return y


def tanh(inputs):
    y = [(np.exp(x) - np.exp(-x)) / float(np.exp(x) + np.exp(-x))
         for x in inputs]
    return y


def softplus(inputs):
    y = [np.log(1 + np.exp(x)) for x in inputs]
    return y


# 經過TensorFlow的激活函數處理的各個y值
y_sigmoid = tf.nn.sigmoid(x)
y_relu = tf.nn.relu(x)
y_tanh = tf.nn.tanh(x)
y_softplus = tf.nn.softplus(x)

# 創建會話
with tf.Session() as sess:
    y_sigmoid, y_relu, y_tanh, y_softplus = sess.run(
        [y_sigmoid, y_relu, y_tanh, y_softplus])
    print(y_sigmoid)
    print(y_relu)
    print(y_tanh)
    print(y_softplus)

    # 創建各個激活函數的圖像
    plt.subplot(221)
    plt.plot(x, y_sigmoid, c='red', label='Sigmoid')
    plt.ylim(-0.2, 1.2)
    plt.legend(loc='best')

    plt.subplot(222)
    plt.plot(x, y_relu, c='red', label='Relu')
    plt.ylim(-1, 6)
    plt.legend(loc='best')

    plt.subplot(223)
    plt.plot(x, y_tanh, c='red', label='Tanh')
    plt.ylim(-1.3, 1.3)
    plt.legend(loc='best')

    plt.subplot(224)
    plt.plot(x, y_softplus, c='red', label='Softplus')
    plt.ylim(-1, 6)
    plt.legend(loc='best')

    # 顯示圖像
    plt.show()