batch_size = 100
#-----------------------------------------------------
#iteration
num_of_itr = 100
for i in range(num_of_itr):
X_batch, T_batch = function1.batch(X_train, T_train, batch_size)
# function1.show_img(X_batch,T_batch,1)
#=========================================
C = function1.convolute(X_batch, F)
# function1.show_img(C,T_batch,1)
Y = function1.affine(C, Wo, Bo, 'softmax')
E = function1.error(Y, T_batch)
E_save = np.append(E_save, E)
Acc = function1.accuracy(Y, T_batch)
accuracy_save = np.append(accuracy_save, Acc)
#=========================================
dWo = np.dot(C.T, (Y - T_batch))
dBo = np.reshape(np.sum(Y - T_batch, axis=0), (1, 10))
delta = np.dot(Y - T_batch, Wo.T)
dF = function1.deconvolute(X_batch, delta)
dF = np.average(dF, axis=0)
dF = np.reshape(dF, (5, 5))
for M in range(batch_size):
img = np.reshape(X_batch[M], (IMG_size, IMG_size))
FM_storage = []
for i in range(FM_size):
for j in range(FM_size):
pick_img = img[i:i + F_size, j:j + F_size]
FM_storage = np.append(FM_storage, np.tensordot(F, pick_img))
FM_bias = FM_storage + Bc #Bias
FM_relu = np.where(FM_bias < 0, 0, FM_bias) #ReLU
FM_batch = np.vstack((FM_batch, FM_relu))
FM_bias_batch = np.vstack((FM_bias_batch, FM_bias))
Y = function1.affine(FM_batch, Wo, Bo, 'softmax')
E = function1.error(Y, T_batch)
E_save = np.append(E_save, E)
Acc = function1.accuracy(Y, T_batch)
accuracy_save = np.append(accuracy_save, Acc)
#======================================================
dWo = np.dot(FM_batch.T, (Y - T_batch))
dBo = np.reshape(np.sum(Y - T_batch, axis=0), (1, 10))
#------------------------------------------------------
delta = np.where(FM_bias_batch <= 0, 0, 1) * np.dot(Y - T_batch, Wo.T)
dF_batch = np.empty((0, F_size**2))
Bo = np.random.randn(1, 10)
learning_rate = 0.001
E_save = []
accuracy_save = []
start_time = time.time()
batch_size = 100
#iteration
num_of_itr = 3000
for i in range(num_of_itr):
X_batch, T_batch = function1.batch(X_train, T_train, batch_size)
H = function1.affine(X_batch, Wh, Bh, 'sigmoid')
Y = function1.affine(H, Wo, Bo, 'softmax')
E = function1.error(Y, T_batch)
E_save = np.append(E_save, E)
Acc = function1.accuracy(Y, T_batch)
accuracy_save = np.append(accuracy_save, Acc)
#=======================
dWo = np.dot(H.T, (Y - T_batch))
dBo = np.reshape(np.sum(Y - T_batch, axis=0), (1, 10))
dWh = np.dot(X_batch.T, H * (1 - H) * np.dot(Y - T_batch, Wo.T))
dBh = np.sum(H * (1 - H) * np.dot(Y - T_batch, Wo.T),
axis=0,