if test_Y[0][i] > 0.5:
prediction_Y[0][i] = 1
else:
prediction_Y[0][i] = 0
return prediction_Y
if __name__ == "__main__":
train_x, train_y, test_x, test_y, list_classes = load_dataset()
#归一化
train_x = train_x / 255
test_x = test_x / 255
#参数初始化
params = model.params_init_model(train_x.shape[0],
2, [5, 1],
init_method="He")
W1 = params["W1"]
b1 = params["b1"]
W2 = params["W2"]
b2 = params["b2"]
learning_rate = 0.01
echo_num = 5000
for echo in range(echo_num):
#正向传播
A1, Z1 = model.l_layer_forward_model(train_x,
W1,
b1,
acitvation="relu") #第1层
A2, Z2 = model.l_layer_forward_model(A1, W2, b2,
acitvation="sigmoid") #第2层
if test_Y[0][i] > 0.5:
prediction_Y[0][i] = 1
else:
prediction_Y[0][i] = 0
return prediction_Y
if __name__ == "__main__":
train_X, train_Y, test_x, test_y, list_classes = load_dataset()
#归一化
train_X = train_X / 255
test_x = test_x / 255
#参数初始化
params = model.params_init_model(train_X.shape[0],
4, [20, 7, 5, 1],
init_method="He")
W1 = params["W1"]
b1 = params["b1"]
W2 = params["W2"]
b2 = params["b2"]
W3 = params["W3"]
b3 = params["b3"]
W4 = params["W4"]
b4 = params["b4"]
learning_rate = 0.0075
echo_num = 1000
for echo in range(echo_num):
batches = model.mini_batch(train_X, train_Y)
for batch in batches:
train_x, train_y = batch
for i in range(test_Y.shape[1]):
if test_Y[0][i] > 0.5:
prediction_Y[0][i] = 1
else:
prediction_Y[0][i] = 0
return prediction_Y
if __name__ == "__main__":
train_x, train_y, test_x, test_y, list_classes = load_dataset()
#归一化
train_x = train_x / 255
test_x = test_x / 255
#参数初始化
params = model.params_init_model(train_x.shape[0], 1, [1])
W1 = params["W1"]
b1 = params["b1"]
learning_rate = 0.001
echo_num = 1000
for echo in range(echo_num):
#正向传播
A1, Z1 = model.l_layer_forward_model(train_x,
W1,
b1,
acitvation="sigmoid") #第1层
#计算代价
J, dJ = model.cost_model(A1, train_y)
#反向传播