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execute_nn.py
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execute_nn.py
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import sys
import combdetection.utils.generator as generator
import combdetection.neuralnet
import combdetection.config
import numpy as np
import pickle
if __name__ == '__main__':
file = sys.argv[1]
load = False
if(len(sys.argv) >=3):
load =True
gen = generator.Generator(file)
X_train, X_test, y_train, y_test= gen.load_traindata()
X_train = np.asarray(X_train)
X_test = np.asarray(X_test)
y_test = np.asarray(y_test)
y_train = np.asarray(y_train)
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))
if not load:
nn = combdetection.neuralnet.NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=50,
l2=0.1,
l1=0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
minibatches=50,
random_state=1)
nn.fit(X_train, y_train, print_progress=True)
pickle.dump(nn, open( "save.p", "wb" ), protocol=pickle.HIGHEST_PROTOCOL)
else:
nn = pickle.load( open( "save.p", "rb" ))
import matplotlib.pyplot as plt
plt.plot(range(len(nn.cost_)), nn.cost_)
plt.ylim([0, 2000])
plt.ylabel('Cost')
plt.xlabel('Epochs * 50')
plt.tight_layout()
# plt.savefig('./figures/cost.png', dpi=300)
plt.show()
batches = np.array_split(range(len(nn.cost_)), 1000)
cost_ary = np.array(nn.cost_)
cost_avgs = [np.mean(cost_ary[i]) for i in batches]
plt.plot(range(len(cost_avgs)), cost_avgs, color='red')
plt.ylim([0, 2000])
plt.ylabel('Cost')
plt.xlabel('Epochs')
#plt.tight_layout()
#plt.savefig('./figures/cost2.png', dpi=300)
plt.show()
y_train_pred = nn.predict(X_train)
acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
print('Training accuracy: %.2f%%' % (acc * 100))
y_test_pred = nn.predict(X_test)
acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
print('Training accuracy: %.2f%%' % (acc * 100))
miscl_img = X_test[y_test != y_test_pred][:25]
correct_lab = y_test[y_test != y_test_pred][:25]
miscl_lab= y_test_pred[y_test != y_test_pred][:25]
labels = combdetection.config.NETWORK_CLASS_LABELS
fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(25):
img = miscl_img[i].reshape(combdetection.config.GENERATOR_SAMPLE_SIZE[0], combdetection.config.GENERATOR_SAMPLE_SIZE[1])
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
right_l =[label for label, enc in labels.items() if enc == correct_lab[i]]
pre_l = [label for label, enc in labels.items() if enc == miscl_lab[i]]
ax[i].set_title('%d) t: %s p: %s' % (i+1, right_l[0] ,pre_l[0]))
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
# plt.savefig('./figures/mnist_miscl.png', dpi=300)
plt.show()