def forward(network, x):
print("##### 順伝播開始 #####")
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
# 1層の総入力
u1 = np.dot(x, W1) + b1
# 1層の総出力
z1 = functions.relu(u1)
# 2層の総入力
u2 = np.dot(z1, W2) + b2
# 2層の総出力
z2 = functions.relu(u2)
# 出力層の総入力
u3 = np.dot(z2, W3) + b3
# 出力層の総出力
y = u3
print_vec("総入力1", u1)
print_vec("中間層出力1", z1)
print_vec("総入力2", u2)
print_vec("出力1", z1)
print("出力合計: " + str(np.sum(z1)))
return y, z1, z2
def forward(network, x):
print ("##### 順伝播開始 #####")
W1, W2, W3 = network ['W1'], network ['W2'], network ['W3']
b1, b2, b3 = network ['b1'], network ['b2'], network ['b3']
# 隠れ層の総入力
u1 = np.dot (x, W1) + b1
# 隠れ層1の総出力
z1 = functions.relu (u1)
# 隠れ層２層への総入力
u2 = np.dot (z1, W2) + b2
# 隠れ層2の出力
z2 = functions.relu (u2)
u3 = np.dot (z2, W3) + b3
z3 = functions.sigmoid (u3)
y = z3
print_vec ("総入力1", u1)
print_vec ("中間層出力1", z1)
print_vec ("総入力2", u2)
print_vec ("出力1", y)
print ("出力合計: " + str (np.sum (y)))
return y, z1
def predict(self, x):
W1, W2, W3, W4 = self.W1, self.W2, self.W3, self.W4
s1 = np.dot(x, W1)
r1 = relu(s1)
s2 = np.dot(r1, W2)
r2 = relu(s2)
s3 = np.dot(r2, W3)
r3 = relu(s3)
s4 = np.dot(r3, W4)
y = softmax(s4)
return y
def predict_relu(network, x):
w1, w2, w3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, w1) + b1
z1 = relu(a1)
a2 = np.dot(z1, w2) + b2
z2 = relu(a2)
a3 = np.dot(z2, w3) + b3
y = softmax(a3)
return y
def test_relu_vector(self):
inputs = [np.array([-1, 0, 1])]
expected = [np.array([0, 0, 1])]
for i in range(len(inputs)):
actual = relu(inputs[i])
npt.assert_array_equal(actual, expected[i])
def forward(network, x):
print("##### 順伝播開始 #####")
W1, W2 = network['W1'], network['W2']
b1, b2 = network['b1'], network['b2']
# 1層の総入力
u1 = np.dot(x, W1) + b1
# 1層の総出力
z1 = functions.relu(u1)
# 2層の総入力
u2 = np.dot(z1, W2) + b2
# 出力値
y = functions.softmax(u2)
print_vec("総入力1", u1)
print_vec("中間層出力1", z1)
print_vec("総入力2", u2)
print_vec("出力1", y)
print("出力合計: " + str(np.sum(y)))
return y, z1
def test_relu_scalar(self):
inputs = [-1, 0, 1]
expected = [0, 0, 1]
for i in range(len(inputs)):
actual = relu(inputs[i])
self.assertEqual(
actual, expected[i],
"expected=[%d] but got actual=[%d]" % (expected[i], actual))
def forward(self, network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
# 勾配
u1 = np.dot(x, W1) + b1
# 活性化関数 Relu関数を使用
z1 = functions.relu(u1)
# 勾配
u2 = np.dot(z1, W2) + b2
# 活性化関数 Relu関数を使用
z2 = functions.relu(u2)
# 勾配
u3 = np.dot(z2, W3) + b3
# 誤差関数(恒等写像)
y = u3
return z1, z2, y
def forward(self, x):
# x->N,(T,D)
self.x1 = x
W1, b1, W2, b2 = self.params
out1 = np.dot(x, W1) + b1
out1 = relu(out1)
# x2->N,(T,D)
self.x2 = out1
out2 = np.dot(out1, W2) + b2
return out2
def forward(network, x):
W1, W2 = network['W1'], network['W2']
b1, b2 = network['b1'], network['b2']
u1 = np.dot(x, W1) + b1
z1 = functions.relu(u1)
u2 = np.dot(z1, W2) + b2
y = functions.softmax(u2)
return z1, y
def predict(network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
#x는 2차원 매개변수W는 4차원
x = x.reshape(1, 1, 28, 28) #x 데이터 성형
print(W1.shape)
N, C, H, W = x.shape
FN, C, FH, FW = W1.shape
out_h = int(1 + (H + 2 * 0 - FH) / 2)
out_w = int(1 + (W + 2 * 0 - FW) / 2)
col = im2col(x, FH, FW, 1, 0)
col_W = W1.reshape(FN, -1).T # 필터 전개
out = np.dot(col, col_W) + b1
out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2)
a1 = out
z1 = relu(a1)
N, C, H, W = z1.shape
out_h = int(1 + (H - 2 * 0 - FH) / 2)
out_w = int(1 + (W - 2 * 0 - FW) / 2)
z1 = im2col(z1, 2, 2, stride=2, pad=0)
z1 = z1.reshape(-1, 2 * 2)
z1 = np.max(z1, axis=1)
print(z1.shape)
print(W2.shape)
print(W3.shape)
a2 = np.dot(z1, W2) + b2
z2 = relu(a2)
a3 = np.dot(z2, W3) + b3
y = softmax(a3)
return y
def forward(network, x):
# print("##### 順伝播開始 #####")
W1, W2 = network['W1'], network['W2']
b1, b2 = network['b1'], network['b2']
u1 = np.dot(x, W1) + b1
z1 = functions.relu(u1)
## 試してみよう
#z1 = functions.sigmoid(u1)
u2 = np.dot(z1, W2) + b2
y = u2
# print_vec("総入力1", u1)
# print_vec("中間層出力1", z1)
# print_vec("総入力2", u2)
# print_vec("出力1", y)
# print("出力合計: " + str(np.sum(y)))
return z1, y
def backward(self, dout):
# dout->N,(T,D) / W1->(D,Df) / W2->(Df,D)
W1, b1, W2, b2 = self.params
N, T, _ = dout.shape
dx2 = np.dot(dout, W2.T)
# dx2->N,(T,Df)
dx2 = relu(dx2)
dW2 = np.matmul(np.transpose(self.x2, (0, 2, 1)), dout)
dW2 = np.nansum(dW2, axis=0)
dx1 = np.dot(dx2, W1.T)
dW1 = np.matmul(np.transpose(self.x1, (0, 2, 1)), dx2)
dW1 = np.nansum(dW1, axis=0)
db2 = np.nansum(np.sum(dout, axis=0), axis=0)
db1 = np.nansum(np.sum(dx2, axis=0), axis=0)
self.grads[0][...] = dW1
self.grads[1][...] = db1
self.grads[2][...] = dW2
self.grads[3][...] = db2
return dx1
out_bin = np.zeros_like(d_bin)
# 時系列全体の誤差
all_loss = 0
# 時系列ループ
for t in range(binary_dim):
# 入力値
X = np.array([a_bin[-t - 1], b_bin[-t - 1]]).reshape(1, -1)
# 時刻tにおける正解データ
dd = np.array([d_bin[binary_dim - t - 1]])
u[:, t + 1] = np.dot(X, W_in) + np.dot(z[:, t].reshape(1, -1), W)
# 中間層の活性化関数を変更してみよう
# z[:,t+1] = functions.sigmoid(u[:,t+1])
z[:, t + 1] = functions.relu(u[:, t + 1])
# y[:,t] = functions.sigmoid(np.dot(z[:,t+1].reshape(1, -1), W_out))
y[:, t] = functions.relu(np.dot(z[:, t + 1].reshape(1, -1), W_out))
# 誤差
loss = functions.mean_squared_error(dd, y[:, t])
# delta_out[:,t] = functions.d_mean_squared_error(dd, y[:,t]) * functions.d_sigmoid(y[:,t])
delta_out[:, t] = functions.d_mean_squared_error(
dd, y[:, t]) * functions.d_relu(y[:, t])
all_loss += loss
out_bin[binary_dim - t - 1] = np.round(y[:, t])
#画像テスト用データインポート
#--------------------------------
import sys, os
from dataset.mnist import load_mnist
from PIL import Image
#--------------------------------
a = np.array([10000.3,2.9,4.0])
y = softmax(a)
print(np.sum(y))
x = np.arange(-5.0, 5.0, 0.1)
y1 = sigmoid(x)
y2 = step_function(x)
y3 = relu(x)
plt.plot(x, y1)
plt.plot(x, y2, 'k--')
plt.plot(x, y3)
plt.ylim(-0.1, 1.1) #図で描画するy軸の範囲を指定
#画像出力
#--------------------------------
plt.savefig('../../../../var/www/html/images/graph.png')
#--------------------------------
# 活性化関数による3層ニューラルネットワークのプロセス
#--------------------------------
def init_network():
network = {}
## 試してみよう_数値の初期化
#b = np.random.rand() # 0~1のランダム数値
#b = np.random.rand() * 10 -5 # -5~5のランダム数値
print_vec("バイアス", b)
# 入力値
x = np.array([2, 3])
print_vec("入力", x)
# 総入力
u = np.dot(x, W) + b
print_vec("総入力", u)
# 中間層出力
z = functions.relu(u)
print_vec("中間層出力", z)
# In[ ]:
# 順伝播（単層・複数ユニット）
# 重み
W = np.array([[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5],
[0.4, 0.5, 0.6]])
## 試してみよう_配列の初期化
#W = np.zeros((4,3))
#W = np.ones((4,3))
#W = np.random.rand(4,3)
#W = np.random.randint(5, size=(4,3))
import numpy as np import numpy.testing as npt from common import functions as function from common import gradient from common import optimizer #test sigmoid X = np.array([-1.0, 1.0, 2.0]) Y = function.sigmoid(X) expected_Y = np.array([0.26894142, 0.73105858, 0.88079708]) npt.assert_array_almost_equal(Y, expected_Y) #test ReLU X = np.array([-0.2, 0.3, 10.0, -2.0]) Y = function.relu(X) expected_Y = np.array([0, 0.3, 10.0, 0]) npt.assert_array_equal(Y, expected_Y) #test softmax a = np.array([0.3, 2.9, 4.0]) Y = function.softmax(a) expected_Y = np.array([0.01821127, 0.24519181, 0.73659691]) npt.assert_array_almost_equal(Y, expected_Y) #test MSE #3번째가 정답 T = np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0]) #3번째일 확률이 가장 높을 것이라고 추정 Y = np.array([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) MSE = function.MSE(Y, T) expected_MSE = 0.0975
