In [1]:
import ANN
import numpy as np
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (18,7)
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from IPython import display
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Input = [[0,0],[0,1],[1,0],[1,1]]
target = [0,1,1,0]
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numLayers = 3
iterations = 5000
eta = 0.3
nn1 = ANN.FNN(numLayers, Input, target, eta=eta)
#output, error = nn1.train(iterations)
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target = nn1.__target__
error = []
output = []
out, e = nn1.train()
error.append(e)
output.append(out)
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plt.ion()
f, ax = plt.subplots(1,3)
im = ax[0].imshow(target, interpolation = 'none', cmap='viridis', origin='lower', aspect='auto', vmin= 0., vmax = 1.)
ax[0].set_xticks([0,1,2,3])
ax[0].set_xticklabels([str(inp) for inp in Input])
ax[0].set_yticks([])
ax[0].set_xlabel("Input")
ax[0].set_title('XOR Classification')
f.canvas.draw()
f.colorbar(im, ax=ax[0])
plt.pause(2)
im = ax[0].imshow(out, interpolation = 'none', cmap='viridis', origin='lower', aspect='auto', vmin= 0., vmax = 1.)
cost = ax[1].plot(error, c='k')
ax[1].set_title("Change in cost Function")
ax[1].set_xlabel("Iterations")
ax[1].set_ylabel("Cost Function")
x = np.linspace(0,1,100)
w0, w1, w2 = nn1.hidden_layers[0].neurons[1].w
w3, w4, w5 = nn1.hidden_layers[0].neurons[2].w
ax[2].plot(x, ((-w1/w2)*x) + (w0/w2))
ax[2].plot(x, ((-w4/w5)*x) + (w3/w5))
ax[2].set_xlim((0,1))
ax[2].set_ylim((0,1))
f.canvas.draw()
In [6]:
for i in range(iterations):
out, e = nn1.train()
output.append(out)
error.append(e)
#print("Output is {}".format(nn1.output_layer.output))
#print(nn1.output_layer.neurons[0].w,
# nn1.output_layer.prev_layer.neurons[0].output,
# nn1.output_layer.prev_layer.neurons[1].output,
# nn1.output_layer.prev_layer.neurons[2].output)
if i % 5 == 0: # Every 10th iteration
try:
im.set_data(out)
ax[1].plot(error, c='k')
w0, w1, w2 = nn1.hidden_layers[0].neurons[1].w
w3, w4, w5 = nn1.hidden_layers[0].neurons[2].w
w6, w7, w8 = nn1.output_layer.neurons[0].w
#ax[2].set_xlim((0,1))
#ax[2].set_ylim((0,1))
ax[2].plot(x, -((w7/w8)*(((-w1/w2)*x) + (w0/w2))) + (w6/w8))
ax[2].plot(x, -((w7/w8)*(((-w4/w5)*x) + (w3/w5))) + (w6/w8))
#f.canvas.draw()
display.display(f)
ax[2].cla()
display.clear_output(wait=True)
plt.pause(0.0001)
except KeyboardInterrupt:
break
plt.ioff()
plt.close()
In [7]:
print nn1.output_layer.neurons[0].w
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