def main():
net = Network(cost="quadratic")
net.append(Dense(2, 5))
net.append(Dense(5, 3))
# train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
# train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def main():
net = Network(cost="quadratic")
net.append(Dense(2, 5))
net.append(Dense(5, 3))
# train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
# train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def main():
net = Network(cost="mse")
net.append(Convolution1D(input_size=6, filter_size=3, n_filters=1, stride_length=1,
activation="sigmoid"))
net.append(MaxPool1D(input_size=4, n_filters=1, pool_size=2, stride_length=2))
train_X = np.asarray([[0.2, -0.3, 0.5, 0.5, 0.6, 0.3]])
train_Y = np.asarray([[[0.0, 1.0]]])
sigma = net.predict(train_X)
print(sigma)
print(net.cost(train_X, train_Y))
db, dw = net.cost_gradient(train_X, train_Y)
def main():
bias_couplings = [
((0, 0), (1, 1))
]
net = Network(cost="quadratic", regularizer=BiasCoupleL2(coeff_lambda=0.25, couplings=bias_couplings))
net.append(Dense(2, 5))
net.append(Dense(5, 3))
train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def main():
bias_couplings = [((0, 0), (1, 1))]
net = Network(cost="quadratic",
regularizer=BiasCoupleL2(coeff_lambda=0.25,
couplings=bias_couplings))
net.append(Dense(2, 5))
net.append(Dense(5, 3))
train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[1.0, 0.0, 0.0]])
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def fd():
net = Network(cost="categorical_cross_entropy")
net.append(Dense(2, 5, activation="tanh"))
net.append(Dense(5, 3, activation="linear"))
net.append(Dense(3, 3, activation="softmax"))
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[1.0, 0.0, 0.0]])
# Finite difference checking
net.cost(train_X, train_Y)
db, dw = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2 * h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2 * h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic w")
for x in dw:
for n in x:
print(n)
fd_w = []
for l in range(len(net.layers)):
lw = []
for w in range(len(net.layers[l].w)):
ww = []
for i in range(len(net.layers[l].w[w])):
orig = net.layers[l].w[w][i]
net.layers[l].w[w][i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].w[w][i] -= 2 * h
fm = net.cost(train_X, train_Y)
ww.append((fp - fm) / (2 * h))
net.layers[l].w[w][i] = orig
lw.append(ww)
fd_w.append(lw)
print("numerical w")
for x in fd_w:
for n in x:
print(n)
def fd():
net = Network(cost="categorical_cross_entropy")
net.append(Dense(2, 5, activation="tanh"))
net.append(Dense(5, 3, activation="linear"))
net.append(Dense(3, 3, activation="softmax"))
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.6, -0.2], [0.8, 0.9], [0.1, 0.1]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])
# Finite difference checking
net.cost(train_X, train_Y)
db, dw = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2*h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2*h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic w")
for x in dw:
for n in x:
print(n)
fd_w = []
for l in range(len(net.layers)):
lw = []
for w in range(len(net.layers[l].w)):
ww = []
for i in range(len(net.layers[l].w[w])):
orig = net.layers[l].w[w][i]
net.layers[l].w[w][i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].w[w][i] -= 2*h
fm = net.cost(train_X, train_Y)
ww.append((fp - fm) / (2*h))
net.layers[l].w[w][i] = orig
lw.append(ww)
fd_w.append(lw)
print("numerical w")
for x in fd_w:
for n in x:
print(n)
def fd():
net = Network(cost="mse")
net.append(Convolution1D(input_size=4, filter_size=2, n_filters=3, stride_length=2,
activation="relu", flatten_output=True))
net.append(Dense(6, 2))
# train_X = np.asarray([[0.2, -0.3, 0.5, 0.6, 0.2]])
# train_Y = np.asarray([[[0.0, 0.0, 1.0]]])
# train_Y = np.asarray([[0.0, 0.0, 1.0]])
# train_Y = np.asarray([[[0.0, 0.0, 1.0], [0.5, 0.5, 0.5]]])
# train_Y = np.asarray([[[0.0, 0.0, 1.0, 0.5, 0.5, 0.5]]])
train_X = np.asarray([[0.2, 0.3, 0.4, 0.7]])
train_Y = np.asarray([[0.0, 1.0]])
train_X = np.asarray([[0.2, 0.3, 0.4, 0.7], [0.1, -0.5, -0.9, 0.3]])
train_Y = np.asarray([[0.0, 1.0], [1.0, 0.0]])
# Finite difference checking
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
db, dw = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2*h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2*h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic w")
for i, x in enumerate(dw):
print(i)
for n in x:
print(n)
fd_w = []
for l in range(len(net.layers)):
lw = []
for w in range(len(net.layers[l].w)):
ww = []
for i in range(len(net.layers[l].w[w])):
orig = net.layers[l].w[w][i]
net.layers[l].w[w][i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].w[w][i] -= 2*h
fm = net.cost(train_X, train_Y)
ww.append((fp - fm) / (2*h))
net.layers[l].w[w][i] = orig
lw.append(ww)
fd_w.append(lw)
print("numerical w")
for i, x in enumerate(fd_w):
print(i)
for n in x:
print(n)
