def generator_net(x, training, opts, name='Generator'):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
sz = opts.img_size // 16
# x is input. 1 x 1 x nz
# state size. sz x sz x (ngf*8)
y = deconv(x, opts.ngf*8, sz, 1, 'valid', 'deconv1')
y = batch_norm(y, training)
y = relu(y)
# state size. (sz*2) x (sz*2) x (ngf*4)
y = deconv(y, opts.ngf*4, 4, 2, 'same', 'deconv2')
y = batch_norm(y, training)
y = relu(y)
# state size. (sz*4) x (sz*4) x (ngf*2)
y = deconv(y, opts.ngf*2, 4, 2, 'same', 'deconv3')
y = batch_norm(y, training)
y = relu(y)
# state size. (sz*8) x (sz*8) x ngf
y = deconv(y, opts.ngf, 4, 2, 'same', 'deconv4')
y = batch_norm(y, training)
y = relu(y)
# state size. (sz*16) x (sz*16) x nc
y = deconv(y, opts.nc, 4, 2, 'same', 'deconv5')
y = tf.nn.tanh(y)
tf.logging.info("Generator output: {}".format(y))
return y
def build_cnn(states, reuse=False, init=None):
"""
Builds ACER network, returns the shared layer
"""
#init = tf.initializers.random_normal(0,0.1)
with tf.variable_scope("shared_policy_net"):
with tf.variable_scope("conv_layer_1"):
conv_l1 = relu(conv2d(states, 8, 4, 32, init))
print(conv_l1)
with tf.variable_scope("conv_layer_2"):
conv_l2 = relu(conv2d(conv_l1, 4, 2, 64, init))
print(conv_l2)
with tf.variable_scope("conv_layer_3"):
conv_l3 = relu(conv2d(conv_l2, 3, 1, 64, init))
print(conv_l3)
with tf.name_scope("flatten"):
conv_l3_flat = tf.layers.flatten(conv_l3)
with tf.variable_scope("shared_fully_connected"):
shared_fc = tf.layers.dense(
inputs=conv_l3_flat,
units=512,
activation=tf.nn.relu,
kernel_initializer=init,
)
return shared_fc
def forwardPropagation(input, weights, bias, originalOutput,
binarizedTruePrediction, prediction, numberOfSamples,
numberOfNeuronsInLayers, classes, optimizer):
#computing output of first hidden layer
h1In = numpy.dot(input[:, :3], weights[0]) + numpy.repeat(
numpy.array([bias[0]]), repeats=[numberOfSamples], axis=0)
h1Output = utils.relu(h1In)
#computing output of second hidden layer
h2In = numpy.dot(h1Output, weights[1]) + numpy.repeat(
numpy.array([bias[1]]), repeats=[numberOfSamples], axis=0)
h2Output = utils.relu(h2In)
#computing output of the output layer
OIn = numpy.dot(h2Output, weights[2]) + numpy.repeat(
numpy.array([bias[2]]), repeats=[numberOfSamples], axis=0)
OOutput = utils.softmax(OIn)
myPredictedValueListAsIntegers = numpy.argmax(OOutput, axis=1)
# Computing overall error only for plotting graph
if prediction == False:
errorForGraph = utils.log_loss(binarizedTruePrediction,
OOutput[:] + 0.00001)
errorForPlottingGraphList.append(errorForGraph)
accuracyScoreForGraph.append(
utils.accuracy_score(originalOutput,
myPredictedValueListAsIntegers))
backPropagation(input, OOutput, originalOutput,
binarizedTruePrediction, h1Output, h2Output,
numberOfNeuronsInLayers, classes, h1In, h2In, OIn,
optimizer)
else:
return myPredictedValueListAsIntegers
def forward(self, x):
"""Forwardpropagation pass"""
# Layer_in
a_in = np.matmul(x, self.w_in.T) + self.b_in
z_in = relu(a_in)
# Layer_rec
a_rec = np.matmul(z_in, self.w_rec.T) + self.b_rec
z_rec = relu(a_rec)
# Layer_link
a_link = np.matmul(np.matmul(x, self.w_reduce), self.w_link.T)
z_link = identity(a_link)
# Layer_out
a_out = np.matmul(z_rec+z_link, self.w_out.T) + self.b_out
y = relu(a_out)
# Set internal state
self._state.update({
'x': x,
'y': y,
'a_out': a_out,
'z_link': z_link,
'a_link': a_link,
'z_rec': z_rec,
'a_rec': a_rec,
'z_in': z_in,
'a_in': a_in,
})
return y
def forward(self, x):
#Layer_in forward pass
self.x = x
B_in = np.matmul(np.ones((BATCH_SIZE, 1)),
self.b_in.reshape(1, P)) # [20, 75]
self.a_in = np.matmul(self.x, self.w_in.transpose()) + B_in # [20, 75]
z_in_numpy = relu(self.a_in)
# Layer_rec forward pass
B_rec = np.matmul(np.ones((BATCH_SIZE, 1)),
self.b_rec.reshape(1, P)) # [20, 75]
self.a_rec = np.matmul(z_in_numpy,
self.w_rec.transpose()) + B_rec # [20, 75]
self.z_rec = relu(self.a_rec)
# Layer_link forward pass
self.x_reduce = x[:, 0::3]
a_link = np.matmul(self.x_reduce, self.w_link.transpose())
self.z_link = identity_func(a_link)
# Layer_out forward pass
self.z_rec_link = self.z_rec + self.z_link
B_out = np.matmul(np.ones((BATCH_SIZE, 1)),
self.b_out.reshape(1, D)) # [20, 225]
self.a_out = np.matmul(self.z_rec_link,
self.w_out.transpose()) + B_out # [20, 225]
y = relu(self.a_out)
return y
def branch1(self, x, numOut, s):
with tf.variable_scope("conv1"):
conv1 = utils.relu(utils.Bn(utils.conv2d(x, numOut/4, d_h=s, d_w=s), training=self.is_training))
with tf.variable_scope("conv2"):
conv2 = utils.relu(utils.Bn(utils.conv2d(conv1, numOut/4, 3, 3), training=self.is_training))
with tf.variable_scope("conv3"):
conv3 = utils.Bn(utils.conv2d(conv2, numOut), training=self.is_training)
return conv3
def residual(self, x, numOut, stride=1, name='res'):
with tf.variable_scope(name):
block = self.branch1(x, numOut, stride)
if x.get_shape().as_list()[3] == numOut:
return utils.relu(tf.add_n([x, block]))
else:
skip = self.branch2(x, numOut, stride)
return utils.relu(tf.add_n([block, skip]))
def gradient(x, y, w1, w2, w3, w4, b1, b2, b3, b4, learning_rate, iterr):
for i in range(iterr):
z1 = np.dot(w1, x) + b1
#print("w1 shape{fgh} x shape{yj}".format(fgh=w1.shape,yj = x.shape))
a1 = relu(z1)
z2 = np.dot(w2, a1) + b2
a2 = relu(z2)
z3 = np.dot(w3, a2) + b3
a3 = relu(z3)
z4 = np.dot(w4, a3) + b4
a4 = sigmoid(z4)
a4 = preds(a4)
dz4 = a4 - y
dw4 = 1 / m * np.dot(dz4, a3.T)
db4 = 1 / m * np.sum(dz4)
da3 = np.dot(w4.T, dz4)
dz3 = da3 * backward_relu(z3)
dw3 = 1 / m * np.dot(dz4, a2.T)
db3 = 1 / m * np.sum(dz3)
da2 = np.dot(w3.T, dz3)
dz2 = da2 * backward_relu(z2)
dw2 = 1 / m * np.dot(dz2, a1.T)
db2 = 1 / m * np.sum(dz2)
da1 = np.dot(w2.T, dz2)
dz1 = da1 * backward_relu(z1)
dw1 = 1 / m * np.dot(dz1, x.T)
db1 = 1 / m * np.sum(dz1)
#print(db1,dw4,db4,dw1)
w1 = w1 - learning_rate * dw1
b1 = b1 - learning_rate * db1
w2 = w2 - learning_rate * dw2
b2 = b2 - learning_rate * db2
w3 = w3 - learning_rate * dw3
b3 = b3 - learning_rate * db3
w4 = w4 - learning_rate * dw4
if (i % 100) == 0:
error = a4 - y
print("Accuracy: " + str(np.sum((a4 == y) / m)))
print("Error:" + str(np.mean(np.abs(error))))
d = {
'w1': w1,
'b1': b1,
'w2': w2,
'b2': b2,
'w3': w3,
'b3': b3,
'w4': w4,
'b4': b4
}
return d
def f_O2(L, p_O2):
l, a, b, c, du, dd = tuple(L)
if p_O2 == 1:
return relu(1 - rat_tdown(L))
elif p_O2 == 2:
return relu(1 - rat_tup(L))
elif p_O2 == 3:
return relu(1 - 2 / (1 / (rat_tdown(L) + 10**-6) + 1 / rat_tup(L)))
else:
return 1
def test(x, w1, w2, w3, w4, b1, b2, b3, b4):
z1 = np.dot(w1, x) + b1
a1 = relu(z1)
z2 = np.dot(w2, a1) + b2
a2 = relu(z2)
z3 = np.dot(w3, a2) + b3
a3 = relu(z3)
z4 = np.dot(w4, a3) + b4
a4 = sigmoid(z4)
a4 = preds(a4)
return a4
def connvolution_process(img):
'''----------------------Reading the image-------------------------------'''
# # print(img.shape) # 3D image
# Converting the image into gray.
img = color.rgb2gray(img)
# # print(img.shape) # 2D image
# io.imshow(img)
# plt.show()
'''----------------------Preparing Filter-------------------------------'''
l1_filter = numpy.zeros((2, 3, 3))
# Vertical ditector Filter
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]])
# Horizontal ditector Filter
l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0], [-1, -1, -1]]])
# # print(l1_filter)
'''---------------------- Convolutional Layer 1 ---------------------------'''
l1_feature_map = conv(img, l1_filter)
# print("l1_feature_map", l1_feature_map.shape)
l1_feature_map_relu = relu(l1_feature_map)
# print("l1_feature_map_relu", l1_feature_map_relu.shape)
l1_feature_map_relu_pool = pooling(l1_feature_map_relu, 2, 2)
# print("l1_feature_map_relu_pool", l1_feature_map_relu_pool.shape)
# print("**End of conv layer 1**\n\n")
'''---------------------- Convolutional Layer 2 ---------------------------'''
l2_filter = numpy.random.rand(3, 5, 5, l1_feature_map_relu_pool.shape[-1])
l2_feature_map = conv(l1_feature_map_relu_pool, l2_filter)
# print("l2_feature_map", l2_feature_map.shape)
l2_feature_map_relu = relu(l2_feature_map)
# print("l2_feature_map_relu", l2_feature_map_relu.shape)
l2_feature_map_relu_pool = pooling(l2_feature_map_relu, 2, 2)
# print("l2_feature_map_relu_pool", l2_feature_map_relu_pool.shape)
# print("**End of conv layer 2**\n\n")
'''---------------------- Convolutional Layer 3 ---------------------------'''
l3_filter = numpy.random.rand(1, 7, 7, l2_feature_map_relu_pool.shape[-1])
l3_feature_map = conv(l2_feature_map_relu_pool, l3_filter)
# print("l3_feature_map", l3_feature_map.shape)
l3_feature_map_relu = relu(l3_feature_map)
# print("l3_feature_map_relu", l3_feature_map_relu.shape)
l3_feature_map_relu_pool = pooling(l3_feature_map_relu, 2, 2)
# print("l3_feature_map_relu_pool", l3_feature_map_relu_pool.shape)
# print("**End of conv layer 3**\n\n")
'''---------------------- Graphing results of convolution ---------------------------'''
draw_layer(img, l1_feature_map, l1_feature_map_relu,
l1_feature_map_relu_pool, l2_feature_map, l2_feature_map_relu,
l2_feature_map_relu_pool, l3_feature_map, l3_feature_map_relu,
l3_feature_map_relu_pool)
'''---------------------- Fully Connected layer ---------------------------'''
# print("**Fully connected layer(Convolutional layer to Fully connected layer)**")
fc = l3_feature_map_relu_pool.reshape(-1)
## print(fc.shape)
return fc
def forward_propagation_with_dropout(X, parameters, keep_prob=0.5):
"""
带有dropout的前向传播
"""
np.random.seed(1)
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
W3 = parameters["W3"]
b3 = parameters["b3"]
# LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID
# 计算第一层输出
Z1 = np.dot(W1, X) + b1
A1 = relu(Z1)
# 开始
# 初始化一个矩阵
D1 = np.random.rand(A1.shape[0], A1.shape[1])
# 标记为0和1
D1 = D1 < keep_prob
# 对于A1中的部分结果丢弃
A1 = np.multiply(A1, D1)
# 保持原来的期望值
A1 /= keep_prob
# 结束
# 计算第二层输出
Z2 = np.dot(W2, A1) + b2
A2 = relu(Z2)
# 开始
D2 = np.random.rand(A2.shape[0], A2.shape[1])
D2 = D2 < keep_prob
A2 = np.multiply(A2, D2)
A2 /= keep_prob
# 结束
# 最后一层输出
Z3 = np.dot(W3, A2) + b3
A3 = sigmoid(Z3)
cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)
return A3, cache
def predict(self, X, y, show_probs=True):
"""
"""
# forward prop through layers
for layer_index in np.arange(1, len(self.structure)):
layer = self.structure[layer_index]
prev_layer = self.structure[layer_index - 1]
if prev_layer['bias_node']:
X = np.concatenate((X, np.ones((X.shape[0], 1))),
axis=1) # change 1. to self.bias?
X = (np.dot(layer['weight_matrix'], X.T)).T
# activation functions
if layer['activation'] == 'relu':
X = relu(X)
elif layer['activation'] == 'softmax':
X = softmax(X.T).T
elif layer['activation'] == 'sigmoid':
X = sigmoid(X)
else:
raise Exception('Activation function not recognised')
predictions = np.argmax(prob_predictions, axis=1)
self.predict_cm = createConfusionMatrix(predictions=predictions,
targets=np.argmax(y_train,
axis=1))
if show_probs == True:
return X
else:
return predictions
def forward(self, X):
if (self.activationFunction == "relu"):
hiddenLayerOutput = relu(X.dot(self.W1) + self.b1)
else:
hiddenLayerOutput = tanh(X.dot(self.W1) + self.b1)
return softmax(hiddenLayerOutput.dot(self.W2) +
self.b2), hiddenLayerOutput
def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok,tgt in batchAndTargets]
tgts = [tgt for ctxtok,tgt in batchAndTargets]
context = self.wvec[ctxtoks,:].reshape((nb,-1))
z = np.dot(context, self.Wz)
a = relu(z)
scores = np.dot(a, self.Wa)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dWa = np.dot(a.T, dscores)
da = np.dot(dscores, self.Wa.T)
dz = drelu(da, a)
dWz = np.dot(context.T, dz)
dcontext = np.dot(dz, self.Wz.T).reshape((nb,-1,self.wdim))
dwvec = np.zeros_like(self.wvec)
for b,ctxtok in enumerate(ctxtoks):
for w,tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b,w]
grads = {}
grads['Wa'] = dWa + self.reg * self.Wa
grads['Wz'] = dWz + self.reg * self.Wz
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
def backpropagate(self, x, y):
gradients_b = [np.zeros(b.shape) for b in self.biases]
gradients_w = [np.zeros(w.shape) for w in self.weights]
#feedforward
a = x
a_list = [x]
z_list = []
for b, w in zip(self.biases[0:-1], self.weights[0:-1]):
z = np.dot(w, a) + b
a = utils.relu(z)
a_list.append(a)
z_list.append(z)
z = np.dot(self.weights[-1], a_list[-1]) + self.biases[-1]
a = utils.softmax(z)
a_list.append(a)
z_list.append(z)
# backward
# for softmax-cross-entropy layer: delta in last layer = result - ground truth
delta = a_list[-1] - y
# update b and w for the last layer L
gradients_b[-1] = delta
gradients_w[-1] = np.dot(delta, a_list[-2].transpose())
# update b and w for the rest of layers L-1, L-2, ...
for l in range(2, self.num_layers):
z = z_list[-l] # lth last layer of z
r_derivative = utils.relu_derivative(z)
# update delta based on delta(l) = transpose of w(l+1) * delta(l+1)
delta = np.dot(self.weights[-l + 1].transpose(),
delta) * r_derivative
gradients_b[-l] = delta
gradients_w[-l] = np.dot(delta, a_list[-l - 1].transpose())
return (gradients_b, gradients_w)
def prediction_baysian_dropout(self, x, k=10):
"""
Runs test time prediction k times to get a variance on the output.
Essentially bayesian dropout.
Input:
x: A numpy array of input data, shape (N, D)
Return:
mean: The mean prediction from the dropout ensemble, shape (N, M)
var: The variance on the prediction from the ensemble, shape (N, M)
"""
N, D = x.shape
outputs = np.zeros((k, N, self.M))
for i in range(k):
h = x # Input into the next layer or previous hidden activation
for l in range(self.n_hidden):
w, b = self.params["w" + str(l)], self.params["b" + str(l)]
h, _ = affine(h, w, b) # Affine layer
h, _ = relu(h) # Activation (ReLU)
# Output layer, simply an affine
outputs[i], _ = affine(h, self.params["w_out"],
self.params["b_out"])
mean = np.mean(outputs)
var = np.var(output)
return mean, var
def linear_activation_forward(A_prev, W, b, activation):
"""
Implement the forward propagation for the LINEAR->ACTIVATION layer
Arguments:
A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
b -- bias vector, numpy array of shape (size of the current layer, 1)
activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
Returns:
A -- the output of the activation function, also called the post-activation value
cache -- a python dictionary containing "linear_cache" and "activation_cache";
stored for computing the backward pass efficiently
"""
if activation == "sigmoid":
# Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
Z, linear_cache = linear_forward(A_prev, W, b)
A, activation_cache = utils.sigmoid(Z)
elif activation == "relu":
# Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
Z, linear_cache = linear_forward(A_prev, W, b)
A, activation_cache = utils.relu(Z)
assert (A.shape == (W.shape[0], A_prev.shape[1]))
cache = (linear_cache, activation_cache)
return A, cache
def inLayerForward(self, x):
B_in = np.ones((BATCH_SIZE, 1)) @ self.b_in # [20, 1] * [1, 75]
self.a_in = x @ self.w_in + B_in # [20, 225] * [225, 75] + [20, 75]
self.z_in = relu(self.a_in) # [20, 75]
return self.z_in
def scalar_input_layer(self, x):
# scalar form method for performance competion
z_in = np.zeros((x.shape[0], P))
for i in range(x.shape[0]):
a_in = np.dot(self.w_in, x[i, :].T) + self.b_in.T.flatten()
z_in[i, :] = relu(a_in)
return z_in
def forward_prop(self, data, parameters, layers):
"""
Forward propogate values
:param data: Input data (train and then test)
:param parameters: Dictionary with initialized weeights and biases
:param layers: Number of layers and neurons
:return:
"""
"""
Z[l] = A[l-1] * W[l] + B[l]
A[l] = G[l](Z[l])
"""
# A0 initialized here and not in init function, so we can run the training data through forward prop
parameters['A' + str(0)] = data
for i in range(1, len(layers)):
parameters['Z' + str(i)] = np.add(np.dot(parameters['A' + str(i - 1)], parameters['W' + str(i)]),
parameters['B' + str(i)])
if i != len(layers) - 1:
parameters['A' + str(i)] = ut.relu(parameters['Z' + str(i)])
else:
# Final Activation is Softmax
parameters['A' + str(i)] = ut.softmax(parameters['Z' + str(i)])
return parameters['A' + str(len(layers) - 1)], parameters
def fprop(self, X):
X = np.array([np.array([float(x) for x in j]) for j in X])
X = X.transpose()
self._ha = np.dot(self._w1, X) + np.repeat(self._b1, len(X[0]), axis=1) # valeur des synapses entre x et hidden
self._hs = utils.relu(self._ha) # valeur hidden
self._oa = np.dot(self._w2, self._hs) + np.repeat(self._b2, len(X[0]), axis=1) # valeur entre hidden et sortie
self._os = utils.softmax(self._oa) # valeur de sortie
def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok, tgt in batchAndTargets]
tgts = [tgt for ctxtok, tgt in batchAndTargets]
context = self.wvec[ctxtoks, :].reshape((nb, -1))
z = np.dot(context, self.Wz)
a = relu(z)
scores = np.dot(a, self.Wa)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dWa = np.dot(a.T, dscores)
da = np.dot(dscores, self.Wa.T)
dz = drelu(da, a)
dWz = np.dot(context.T, dz)
dcontext = np.dot(dz, self.Wz.T).reshape((nb, -1, self.wdim))
dwvec = np.zeros_like(self.wvec)
for b, ctxtok in enumerate(ctxtoks):
for w, tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b, w]
grads = {}
grads['Wa'] = dWa + self.reg * self.Wa
grads['Wz'] = dWz + self.reg * self.Wz
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
def prediction(self, x, z):
"""
Compute prediction for the fully-connected net as test time (without
saving cache and no-dropout).
Input:
x: A numpy array of input data, shape (N, D)
z: Diff between y-y_des for the func approx (N, M) (1,2) in this case
Return:
output: Output prediction/prediction of label, shape (N, M)
"""
h = x # Input into the next layer or previous hidden activation
for l in range(self.n_hidden):
l = str(l)
w = self.params["w" + l]
b = self.params["b" + l]
h, _ = affine(h, w, b) # Affine layer
h, _ = relu(h) # Activation (ReLU)
# Output layer, simply an affine
output, cache = affine(h, self.params["w_out"], self.params["b_out"])
# Technically this is not the real z but the 1/N term only scales z (we
# can think of this as equivalent to scaling β by 1/N).
# This is to match how dout works in NeuralNetOffline (see line: 190)
N, D = x.shape
z = z / N
# Only trainable paramters in the adaptive case are the last layer weights
# So we only update the output layer weights (using e-mod)
_, dw, db = self.w_hat_dot_e_mod(z, cache)
# Update the weights
self.params["w_out"] -= self.beta * dw
self.params["b_out"] -= self.beta * db
return output
def vector_input_layer(self, x):
# vector form method for performance competion
B_in = np.matmul(np.ones((BATCH_SIZE, 1)),
self.b_in.reshape(1, P)) # [20, 75]
a_in = np.matmul(x, self.w_in.transpose()) + B_in # [20, 75]
z_in_numpy = relu(a_in)
return z_in_numpy
def inference(self, x):
with tf.variable_scope("conv0"):
conv1 = utils.relu(utils.Bn(utils.conv2d(x, 64, 7, 7, 2, 2, bias=True), training=self.is_training))
with tf.name_scope("pool1"):
pool1 = utils.max_pool(conv1, 3, 3, 2, 2)
with tf.variable_scope("group0"):
res2a = self.residual(pool1, 256, name='block0')
res2b = self.residual(res2a, 256, name='block1')
res2c = self.residual(res2b, 256, name='block2')
with tf.variable_scope("group1"):
res3a = self.residual(res2c, 512, 2, name='block0')
res3b = self.residual(res3a, 512, name='block1')
res3c = self.residual(res3b, 512, name='block2')
res3d = self.residual(res3c, 512, name='block3')
with tf.variable_scope("group2"):
res4a = self.residual(res3d, 1024, 2, name='block0')
res4b = self.residual(res4a, 1024, name='block1')
res4c = self.residual(res4b, 1024, name='block2')
res4d = self.residual(res4c, 1024, name='block3')
res4e = self.residual(res4d, 1024, name='block4')
res4f = self.residual(res4e, 1024, name='block5')
with tf.variable_scope("group3"):
res5a = self.residual(res4f, 2048, 2, name='block0')
res5b = self.residual(res5a, 2048, name='block1')
res5c = self.residual(res5b, 2048, name='block2')
with tf.name_scope("pool5"):
pool5 = utils.global_pool(res5c)
with tf.variable_scope("linear"):
dropout = tf.nn.dropout(pool5, keep_prob=self.keep_prob)
out = utils.linear(dropout, 1000)
return out
def prediction_save_cache(self, x):
"""
Compute prediction for the fully-connected net and save intermediate
activations.
N samples, D dims per sample, each sample is a row vec, M is the dims of
y/prediction
Input:
x: A numpy array of input data, shape (N, D)
Return:
output: Output prediction/prediction of label, shape (N, M)
caches: Saved intermediate activations for use in backprop
"""
caches = {}
h = x # Input into the next layer or previous hidden activation
for l in range(self.n_hidden):
l = str(l)
w, b = self.params["w" + l], self.params["b" + l]
h, caches["affine" + l] = affine(h, w, b) # Affine layer
h, caches["relu" + l] = relu(h) # Activation (ReLU)
# Dropout layer (train-time dropout)
h, caches["dropout" + l] = dropout(h, self.dropout)
# Output layer, simply an affine
output, cache = affine(h, self.params["w_out"], self.params["b_out"])
caches["affine_out"] = cache
return output, caches
def forward_propagation(X, parameters):
"""正向传播
"""
cache = dict()
L = len(parameters) // 2
cache['A{}'.format(0)] = X # A0 = X
# 第1~(L-1)层，LINEAR -> RELU -> LINEAR -> RELU...
for l in range(1, L):
W = parameters['W{}'.format(l)]
b = parameters['b{}'.format(l)]
Z = np.dot(W, cache['A{}'.format(l - 1)]) + b # Zl = Wl * A(l-1) + bl
A = relu(Z) # 该层的A值
cache['W{}'.format(l)] = W
cache['b{}'.format(l)] = b
cache['Z{}'.format(l)] = Z
cache['A{}'.format(l)] = A
# 第L层，LINEAR -> SIGMOID
WL = parameters['W{}'.format(L)]
bL = parameters['b{}'.format(L)]
ZL = np.dot(WL, cache['A{}'.format(L - 1)]) + bL
AL = sigmoid(ZL)
cache['W{}'.format(L)] = WL
cache['b{}'.format(L)] = bL
cache['Z{}'.format(L)] = ZL
cache['A{}'.format(L)] = AL
return AL, cache
def __init__(self, rng, input, filter_shape, image_shape,
poolsize=(2,2), params_W=None, params_b=None,
mu=0, sigma=0.1, bias_val=0.5, activation_mode=0):
assert image_shape[1] == filter_shape[1]
self.input = input
# if params_W is not given, generate random params_W
if params_W == None:
self.W = theano.shared(
numpy.asarray(
rng.normal(mu, sigma, filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
else:
self.W = theano.shared(
numpy.asarray(params_W,dtype=theano.config.floatX), borrow=True)
# if params_b is not given, generate random params_b
if params_b == None:
b_values = bias_val * numpy.ones((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
else:
self.b = theano.shared(
numpy.asarray(params_b,dtype=theano.config.floatX), borrow=True
)
self.momentum_W = theano.shared(
numpy.zeros(filter_shape, dtype=theano.config.floatX),
borrow=True
)
self.momentum_b = theano.shared(
numpy.zeros((filter_shape[0],), dtype=theano.config.floatX),
borrow=True
)
# feature maps after convolution
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
image_shape=image_shape
)
# feature maps after pooling
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=poolsize,
ignore_border=True
)
# output of layer, activated pooled feature maps
if activation_mode == 0:
self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
elif activation_mode == 1:
self.output = relu(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.params = [self.W, self.b]
def _process_layers(self, weights, data, learning=True):
for W, mean, std in self._generate_layers(weights):
data = normalize(data, mean, std)
data = relu(data)
if learning and self.dropout is not None:
data = dropout(data, self.dropout)
data = np.dot(data, W)
return data
def fprop(self, X):
X = np.array([[float(x)] for x in X])
self._ha = np.dot(
self._w1, X) + self._b1 # valeur des synapses entre x et hidden
self._hs = utils.relu(self._ha) # valeur hidden
self._oa = np.dot(self._w2,
self._hs) + self._b2 # valeur entre hidden et sortie
self._os = utils.softmax(self._oa) # valeur de sortie
def linear_activation_forward(a_prev, w, b, activation):
z, linear_cache = linear_forward(a_prev, w, b)
if activation == "sigmoid":
a, activation_cache = sigmoid(z)
else:
a, activation_cache = relu(z)
cache = (linear_cache, activation_cache)
return a, cache
def predict(self, X, learning):
hiddenLayerOutput = relu(X.dot(self.W1) + self.b1)
output = hiddenLayerOutput.dot(self.W2) + self.b2
if (learning):
probs = softmax(output)
action = np.where(probs == np.random.choice(probs, 1, p=probs))
return (int(action[0]))
return np.argmax(output)
def f(X):
n_samples = X.shape[0]
input = X.reshape((n_samples, n_feats[0], image_row, image_col))
conv_out = T.nnet.conv2d(input, P.W_input_conv)
pool_out_= max_pool_2d(conv_out, (pool_row, pool_col))
pool_out = pool_out_.flatten(2) + P.b_pool_out
hidden = relu(T.dot(pool_out, P.W_pool_out_hidden) + P.b_hidden)
output = T.dot(hidden, P.W_hidden_output) + P.b_output
return output.astype(theano.config.floatX)
def activate(self, activation):
if(self.visible_type == "SIGMOID"):
m_output = utils.sigmoid(activation)
elif(self.visible_type == "RELU"):
m_output = utils.relu(activation)
elif(self.visible_type == "LEAKY_RELU"):
m_output = utils.leaky_relu(activation)
elif(self.visible_type == "LINEAR"):
m_output = activation
else:
raise NotImplemented("Unrecogonised hidden type")
return m_output
def layers(x, window, dim_emb, dim_hidden, n_layers, activation=tanh):
params = []
zero = T.zeros((1, 1, dim_emb * window), dtype=theano.config.floatX)
def zero_pad_gate(matrix):
return T.neq(T.sum(T.eq(matrix, zero), 2, keepdims=True), dim_emb * window)
for i in xrange(n_layers):
if i == 0:
W = theano.shared(sample_weights(dim_emb * window, dim_hidden))
h = T.max(zero_pad_gate(x) * relu(T.dot(x, W)), 1)
# h = T.max(T.dot(x, W), 1)
else:
W = theano.shared(sample_weights(dim_hidden, dim_hidden))
h = activation(T.dot(h, W))
params.append(W)
return h, params
def fprop(self, X):
X = np.array([[float(x)] for x in X])
self._ha = np.dot(self._w1, X) + self._b1 # valeur des synapses entre x et hidden
self._hs = utils.relu(self._ha) # valeur hidden
self._oa = np.dot(self._w2, self._hs) + self._b2 # valeur entre hidden et sortie
self._os = utils.softmax(self._oa) # valeur de sortie
def train(self, data_train, initialmomentum, finalmomentum, learn_rate_w, learn_rate_visb, learn_rate_hidb, weightcost): learning_rate = learn_rate_w self.weightcost = weightcost N = np.shape(data_train)[0] num_visible = np.shape(data_train)[1] total_batches = np.int(np.ceil(N/self.mini_batch)) m_visible_biases = np.zeros(num_visible) m_hidden_biases = np.zeros(self.hidden_nodes) m_weights = np.random.randn(num_visible, self.hidden_nodes) * 0.01 momentum_weights = np.zeros_like(m_weights) momentum_visible_biases = np.zeros_like(m_visible_biases) momentum_hidden_biases = np.zeros_like(m_hidden_biases) momentum_rate = finalmomentum best_rmsd = None rmsd_logger=[] for i in range(self.iterations): start = (i % total_batches) * self.mini_batch end = start + self.mini_batch if end >= N : end = N - 1 #print start, end batch = data_train[start:end] batch = batch weights, visible_biases, hidden_biases = self._CD1(batch, m_weights, m_visible_biases, m_hidden_biases) momentum_weights = momentum_rate * momentum_weights + weights momentum_visible_biases = momentum_rate * momentum_visible_biases + visible_biases momentum_hidden_biases = momentum_rate * momentum_hidden_biases + hidden_biases m_weights += momentum_weights * learning_rate m_visible_biases += momentum_visible_biases * learning_rate m_hidden_biases += momentum_hidden_biases * learning_rate # Reconstruction error, no sampling done, using raw probability output = np.dot(data_train, m_weights) \ + np.tile(hidden_biases, (N,1)) if self.visible_type == "SIGMOID": hidden_state = u.sigmoid(output) elif self.visible_type == "RELU": hidden_state = u.relu(output) elif self.visible_type == "LINEAR": hidden_state = output reconstruction = np.dot(hidden_state, m_weights.T) + \ + np.tile(visible_biases, (N,1)) if self.visible_type == "SIGMOID": reconstruction = u.sigmoid(reconstruction) elif self.visible_type == "RELU": reconstruction = u.relu(reconstruction) ################################################## err = (data_train-reconstruction)*(data_train-reconstruction) rmsd = np.sqrt(np.mean(err*err)) rmsd_logger.append(rmsd) print "Epoch %4d/%4d, RMS deviation = %7.4f" % (i + 1, self.iterations, rmsd) if self.early_stop: if best_rmsd is None or (rmsd - best_rmsd) / best_rmsd < -1e-3 : best_weights = m_weights best_hidden_biases = hidden_biases best_visible_biases = visible_biases best_rmsd = rmsd iter_since_best = 0 else : iter_since_best += 1 if iter_since_best >= self.max_epoch_without_improvement : print "Early stop -- best epoch %d / %d, RMS deviation = %7.4f" % ( i + 1 - iter_since_best, self.iterations, best_rmsd) break else: best_weights = m_weights best_hidden_biases = hidden_biases best_visible_biases = visible_biases best_rmsd = rmsd iter_since_best = 0 #print ' rmsd = ', rmsd self.weights = best_weights self.hidden_biases = best_hidden_biases self.visible_biases = best_visible_biases rmsd_history = np.asarray(rmsd_logger) if iter_since_best > 0 : self.train_history = rmsd_history[:-1*iter_since_best] else : self.train_history = rmsd_history
def train(self, data_train, initialmomentum, finalmomentum, learn_rate_w, learn_rate_visb, learn_rate_hidb, weightcost): Nd, numdims = np.shape(data_train) N = self.mini_batch n_tot_batches = np.int(np.ceil(Nd/self.mini_batch)) vishid = 0.01 * np.random.randn(numdims, self.hidden_nodes) hidbiases = np.zeros(self.hidden_nodes) visbiases = np.zeros(numdims) vishidinc = np.zeros_like(vishid) hidbiasinc = np.zeros_like(hidbiases) visbiasinc = np.zeros_like(visbiases) """ pos_hidprobs = np.zeros([N, self.hidden_nodes]) neg_hidprobs = np.zeros_like(pos_hidprobs) pos_prods = np.zeros_like(vishid) neg_prods = np.zeros_like(vishid) """ batchposidprobs = np.empty([N, self.hidden_nodes, n_tot_batches]) rmsd_logger = [] best_weights = np.zeros_like(vishid) best_hidden_biases = np.zeros_like(hidbiases) best_rmsd = None iter_since_best = 0 for epoch in range(self.iterations): errsum = 0 rmsd = 0 ##print "Epoch %d / %d" % (epoch + 1, self.iterations) for batch in range(n_tot_batches): #print " epoch %d / %d -- batch %d / %d" % (epoch + 1, self.iterations,\ # batch + 1, n_tot_batches) start = (batch % n_tot_batches) * self.mini_batch end = start + self.mini_batch if end >= Nd : end = Nd data = data_train[start:end] ## START POSITIVE PHASE ################################################## nw = np.dot(data, vishid) + np.tile(hidbiases, (N, 1)) if self.hidden_type == "SIGMOID": pos_hidprobs = utils.sigmoid(nw) if self.hidden_type == "RELU": pos_hidprobs = utils.relu(nw) elif self.hidden_type == "LINEAR": pos_hidprobs = nw if epoch >= self.iterations - 1: batchposidprobs[:,:,batch] = pos_hidprobs pos_prods = np.dot(data.T, pos_hidprobs) pos_hidact = np.sum(pos_hidprobs, 0) pos_visact = np.sum(data, 0) ## END OF POSITIVE PHASE ################################################ if self.hidden_type == "SIGMOID" or self.hidden_type == "RELU": ran = np.random.rand(N, self.hidden_nodes) pos_hidstates = pos_hidprobs > ran elif self.hidden_type == "LINEAR": ran = np.random.randn(N, self.hidden_nodes) pos_hidstates = pos_hidprobs + ran ## START NEGATIVE PHASE ################################################# nw = np.dot(pos_hidstates,vishid.T) + np.tile(visbiases, (N,1)) # TODO: Do this only if visible type is sigmoid see C++ line 262 and next if self.visible_type == "SIGMOID": neg_data = utils.sigmoid(nw) if self.visible_type == "RELU": neg_data = utils.relu(nw) else: neg_data = nw nw = np.dot(neg_data, vishid) + np.tile(hidbiases, (N, 1)) if self.hidden_type == "SIGMOID": neg_hidprobs = utils.sigmoid(nw) if self.hidden_type == "RELU": neg_hidprobs = utils.relu(nw) else: neg_hidprobs = nw neg_prods = np.dot(neg_data.T, neg_hidprobs) neg_hidact = np.sum(neg_hidprobs, 0) neg_visact = np.sum(neg_data, 0) ## END OF NEGATIVE PHASE ################################################ errsum += np.sum((data-neg_data)*(data-neg_data)) rmsd += np.sqrt(np.mean((data-neg_data)*(data-neg_data))) #print rmsd; exit() if epoch > 5: momentum = finalmomentum else: momentum = initialmomentum ## UPDATE WEIGHTS AND BIASES ############################################ vishidinc = momentum * vishidinc + learn_rate_w * \ ((pos_prods - neg_prods)/N - weightcost * vishid) visbiasinc = momentum * visbiasinc + learn_rate_visb/N * (pos_visact - neg_visact) hidbiasinc = momentum * hidbiasinc + learn_rate_hidb/N * (pos_hidact - neg_hidact) vishid += vishidinc visbiases += visbiasinc hidbiases += hidbiasinc print "Epoch %4d/%4d, RMS deviation = %7.4f" % (epoch + 1, self.iterations, rmsd) rmsd_logger.append(rmsd) if self.early_stop: if best_rmsd is None or (rmsd - best_rmsd) / best_rmsd < -1e-3 : best_weights = vishid best_hidden_biases = hidbiases best_visible_biases = visbiases best_rmsd = rmsd iter_since_best = 0 else : iter_since_best += 1 if iter_since_best >= self.max_epoch_without_improvement : print "Early stop -- best epoch %d / %d, RMS deviation = %7.4f" % ( epoch + 1 - iter_since_best, self.iterations, best_rmsd) break else: best_weights = vishid best_hidden_biases = hidbiases best_visible_biases = visbiases best_rmsd = rmsd iter_since_best = 0 #print ' rmsd = ', rmsd self.weights = best_weights self.hidden_biases = best_hidden_biases self.visible_biases = best_visible_biases rmsd_history = np.asarray(rmsd_logger) if iter_since_best > 0 : self.train_history = rmsd_history[:-1*iter_since_best] else : self.train_history = rmsd_history