working simple autoencoder

tests/network.py

@@ -0,0 +1,80 @@
+from matplotlib import pyplot as plt
+
+WEIGTHS = [
+    [  # L1
+        [0.7095146, -0.4351325, 0.38553882, 0.84955347, -0.8693629, 0.1586302, -0.16126093, 0.09219088],
+        [-0.41895103, 1.0214638, -1.0607314, 0.32464203, 0.8418823, 0.01737848, 0.5317601, -0.624525],
+        [-0.08075078, 0.14494987, 0.01327306, 0.8879888, 0.60206324, 0.75329006, 0.34316933, -0.61903083],
+        [-0.5218736, -0.78134096, -0.28972188, 0.00756884, -0.78290594, -0.57819647, -0.7074082, -0.87057704],
+    ],
+    [  # L2
+        [0.36770403, -0.17659009, -0.04314173, -0.46864474, -0.37941265, -0.78606385, 0.11138931, 0.23097174],
+        [-0.78046024, 0.13901198, 0.14361085, 0.36618456, 0.45330787, 0.24032554, 0.48950365, 0.30290592],
+        [0.3979908, -0.45248222, 0.6259473, -0.45595396, -0.12066315, -0.4472755, 0.12998834, -0.596446],
+        [0.5494289, -0.7894139, 0.3571782, -0.39789405, 0.5636705, -0.24661142, 0.4947537, -0.40108407],
+        [-0.13859335, -0.81092286, -0.38011226, 0.73964316, 0.68990386, -0.2698564, 0.516593, 0.12246455],
+        [0.40053025, -0.521815, 0.01378736, -0.30294785, 0.6543718, -0.8365823, 0.82281274, -0.47260976],
+        [0.08249452, 0.30632392, 0.05794358, 0.2482118, 0.86367106, -0.13674814, 0.04789656, -0.55030185],
+        [-0.32528356, -0.3143816, 0.09667788, -0.2127953, -0.5707757, -0.39799848, 0.30206403, 0.44481543]
+    ],
+    [  # L3
+        [0.5724262, 0.30391088],
+        [0.5853241, -0.92324615],
+        [0.3748752, 0.8088594],
+        [-0.892384, -1.0522624],
+        [-1.0270239, 0.07374455],
+        [0.2170913, -0.550893],
+        [-0.07271451, 0.8194236],
+        [0.14661156, -0.62796086]
+    ]
+]
+BIAS = [
+    # L1
+    [0.01425434, -0.06219335, -0.0201127, -0.04791382, -0.04360008, -0.05311861, -0.01731363, -0.00014839],
+    # L2
+    [0.03480967, 0.06208326, -0.01576317, -0.00037753, -0.03940378, 0.05157978, -0.02775403, 0.04540931],
+    # L3
+    [0.03787775, -0.03655371],
+]
+
+
+def relu(x):
+    return x if x > 0 else 0
+
+
+def sigm(x):
+    ex = 2.7182818 ** x
+    return ex / (ex + 1)
+
+
+def linr(x):
+    return x
+
+
+def process(value):
+    if value > 3 or value < 0:
+        raise ValueError("Value not in between 0 and 3")
+    onehot_mat = [
+        [0, 0, 0, 1],
+        [0, 0, 1, 0],
+        [0, 1, 0, 0],
+        [1, 0, 0, 0],
+    ]
+    L0 = onehot_mat[value]
+    L1 = [linr(sum([L0[j] * WEIGTHS[0][j][i] for j in range(len(L0))]) + BIAS[0][i]) for i in range(8)]
+    L2 = [linr(sum([L1[j] * WEIGTHS[1][j][i] for j in range(len(L1))]) + BIAS[1][i]) for i in range(8)]
+    L3 = [sigm(sum([L2[j] * WEIGTHS[2][j][i] for j in range(len(L2))]) + BIAS[2][i]) for i in range(2)]
+    return L3
+
+
+if __name__ == '__main__':
+    plt.plot(*process(0), 'x')
+    plt.plot(*process(1), 'x')
+    plt.plot(*process(2), 'x')
+    plt.plot(*process(3), 'x')
+    plt.xlabel('Real')
+    plt.ylabel('Imaginary')
+    plt.xlim([0, 1])
+    plt.ylim([0, 1])
+    plt.grid()
+    plt.show()