import matplotlib
matplotlib.use('nbagg')

import keras.backend as K
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf

sess = tf.InteractiveSession()

activations = ['sigmoid', 'tanh', 'ReLU']

x = tf.linspace(-5., 5., 100, name="linspace")

y = list()
y.append(K.tanh(x))
y.append(K.relu(x))
y.append(K.sigmoid(x))

plt.figure(figsize=(10,12))
idx = 0

for _y in y:
    _y = tf.Print(_y, [_y])
    
    aux = _y.eval()
    
    plt.subplot(3,1,idx+1)
    plt.plot(np.linspace(-5, 5, 100), aux)
    plt.xlabel(name[idx])
    plt.ylim([np.min(aux)-0.025, np.max(aux)+0.025])
    
    idx += 1
    
    pass

plt.show()