def T6():
'''
Tests basic functionality of CNNC
'''
A = np.random.rand(32, 9, 9, 3)
Y = np.array((16 * [1]) + (16 * [0]))
ws = [('C', [3, 3, 3, 4], [1, 1, 1, 1]), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('F', 16), ('F', 2)]
a = CNNC([9, 9, 3], ws, maxIter = 12, name = "cnnc1")
a.fit(A, Y)
a.score(A, Y)
a.predict(A)
return True
('C', [8, 5, NC, NC], [1, 8, 5, 1]), ('R', [-1, 64, NC])]
#Create the neural network in TensorFlow
cnnc = CNNC(IS,
ws,
batchSize=64,
learnRate=5e-5,
maxIter=4,
reg=1e-5,
tol=1e-2,
verbose=True)
if not cnnc.RestoreModel('TFModel/', 'ocrnet'):
A, Y, T, FN = LoadData()
ss = ShuffleSplit(n_splits=1, random_state=42)
trn, tst = next(ss.split(A))
#Fit the network
cnnc.fit(A[trn], Y[trn])
#The predictions as sequences of character indices
YH = []
for i in np.array_split(np.arange(A.shape[0]), 32):
YH.append(cnnc.predict(A[i]))
YH = np.vstack(YH)
#Convert from sequence of char indices to strings
PS = np.array([''.join(YHi) for YHi in YH])
#Compute the accuracy
S1 = SAcc(PS[trn], T[trn])
S2 = SAcc(PS[tst], T[tst])
print('Train: ' + str(S1))
print('Test: ' + str(S2))
for PSi, Ti, FNi in zip(PS, T, FN):
print(FNi + ': ' + Ti + ' -> ' + PSi)
cnnc.SaveModel(os.path.join('TFModel', 'ocrnet'))