def test_forward():
X = np.zeros((2,2))
X[0,0] = 1
test = CNN(vector_size=2)
W1, dict = test.initialize_weights()
_Ws,_Bs, CM1,B1,CM2,B2, FCL = dict['Ws'], dict['Ws_bias'], dict['CM1'], dict['B1'], dict['CM2'], dict['B2'], dict['FCL']
z0, a0, z1, a1, z2, a2, z3, a3 = test.forward(X)
print X, u'\n'
print z0, u'\n'
print a0, u'\n'
for i in range(test.fmap1):
print 'CM1',CM1[i], u'\n'
print 'BM1',B1[i], u'\n'
print 'z1',z1[i], u'\n'
print 'a1',a1[i], u'\n'
print u'\n\n'
for i in range(test.fmap2):
for k in range(test.fmap1):
print 'CM2',CM2[i][k], u'\n'
print 'B2',B2[i], u'\n'
print 'z2',z2[i], u'\n'
print 'a2',a2[i], u'\n'
print u'\n\n'
print 'z3',z3, u'\n'
print 'a3',a3, u'\n'
def test_forward():
X = np.zeros((2,2))
X[0,0] = 1
test = CNN(vector_size=2)
W1, _B1, CM1,B1,CM2,B2, FCL = test.initialize_weights()
z0, a0, z1, a1, z2, a2, z3, a3 = test._forward(X,W1, _B1,CM1,B1,CM2,B2,FCL)
print X, u'\n'
print z0, u'\n'
print a0, u'\n'
for i in range(test.feature_maps_number_layer1):
print 'CM1',CM1[i], u'\n'
print 'BM1',B1[i], u'\n'
print 'z1',z1[i], u'\n'
print 'a1',a1[i], u'\n'
print u'\n\n'
for i in range(test.feature_maps_number_layer2):
for k in range(test.feature_maps_number_layer1):
print 'CM2',CM2[i][k], u'\n'
print 'B2',B2[i], u'\n'
print 'z2',z2[i], u'\n'
print 'a2',a2[i], u'\n'
print u'\n\n'
print 'z3',z3, u'\n'
print 'a3',a3, u'\n'
def test_cost(self):
X = np.random.rand(4,2)
y = np.array([0,1,0,0,0,0,0,0,0,0,0,0,0,0,0])
test = CNN(vector_size=4)
CM1,B1,CM2,B2, FCL = test.initialize_weights()
print test.function(X, y, CM1,B1,CM2,B2, FCL)
def test_function_prime(self):
X = np.random.rand(4,2)
y = np.array([0,1,0,0,0,0,0,0,0,0,0,0,0,0,0])
test = CNN(vector_size=4)
CM1,B1,CM2,B2, FCL = test.initialize_weights()
d1fm, d2, d3 = test.function_prime(X, y)
for i in range(test.fmap1):
print d1fm[i], u'\n'
print d2, u'\n'
print d3, u'\n'
def test_new_pack_unpack():
test = CNN(vector_size=4)
A, B, C, D, E, F, G = test.unpack_weights(test.weights)
print A == test.w2v_transf_matrix_1
print '\n\n'
print B == test.w2v_transf_bias_1
print '\n\n'
for x in C.keys():
print C[x] == test.CM1[x]
print '\n\n'
for x in D.keys():
print D[x]== test.B1[x]
print '\n\n'
for x in E.keys():
for y in E[x].keys():
print E[x][y]==test.CM2[x][y]
print '\n\n'
for x in F.keys():
print F[x]== test.B2[x]
print '\n\n'
print G == test.FCL
print '\n\n'
def test_gradient_check(e):
X = np.random.rand(4,2)
y = np.random.permutation(np.array([0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]))
test = CNN(vector_size=4, num_Ws=4)
return test.gradient_check()
def test_ReLU():
A = np.random.rand(4,4)-0.5
test = CNN(vector_size=100)
print A,'\n',test.ReLU(A),'\n', test.ReLU_prime(A)
def test_fit():
test_data_file = np.load("../training_arrays-1000.npz")
X, y, z= test_data_file['X_train'],test_data_file['y_train'], test_data_file['z_train']
test = CNN(vector_size=100)
test.fit(X,z)
def test_cnn_with_transf(vector_size=4):
cnn = DeepCNN(vector_size=vector_size, num_labels=5)
err = cnn.gradient_check()
print 'found', err, 'errors...'
assert err == 0
def test_gradient_check(e):
X = np.random.rand(4,2)
y = np.array([0,1,0,0,0,0,0,0,0,0,0,0,0,0,0])
test = CNN(vector_size=4)
W1,_B1, CM1,B1,CM2,B2,FCL = test.initialize_weights()
dW1, d_B1, dCM1, dB1, dCM2, dB2, dFCL = test.function_prime(X, y,W1, _B1, CM1,B1,CM2,B2,FCL)
# Through FCL
for i in range(test.num_labels):
for j in range(test.kmax*test.feature_maps_number_layer2*test.folding_width + 1):
FCL[i,j] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
FCL[i,j] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dFCL[i,j]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
print J, backprop
FCL[i,j] += e
# #Through CL2
for i in range(test.feature_maps_number_layer2):
for j in range(test.feature_maps_number_layer1):
for n in range(test.vector_size):
for p in range(test.second_kernel_size):
CM2[i][j][n,p] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
CM2[i][j][n,p] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dCM2[i][j][n,p]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
print J, backprop
CM2[i][j][n,p] += e
for i in range(test.feature_maps_number_layer2):
for j in range(test.folding_width):
B2[i][j] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
B2[i][j] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dB2[i][j]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
print J, backprop
B2[i][j] += e
# Through CL1
for i in range(test.feature_maps_number_layer1):
for j in range(test.vector_size):
B1[i][j] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
B1[i][j] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dB1[i][j]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
print J, backprop
B1[i][j] += e
for i in range(test.feature_maps_number_layer1):
for u in range(test.vector_size):
for v in range(test.first_kernel_size):
CM1[i][u,v] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
CM1[i][u,v] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dCM1[i][u,v]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
print J, backprop
CM1[i][u,v] += e
# Through W2VTransf
for i in range(test.vector_size):
for j in range(X.shape[1]):
W1[i,j] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
W1[i,j] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = dW1[i,j]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
W1[i,j] += e
# Through W2VTransf
for i in range(test.vector_size):
_B1[i] += e
Jpos = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
_B1[i] -= 2*e
Jneg = test.function(X,y,W1, _B1,CM1,B1,CM2,B2,FCL)
J = (Jpos - Jneg)/(2*e)
backprop = d_B1[i]
if (J-backprop)/(J+backprop)>e:
print (J-backprop)/(J+backprop)
_B1[i] += e