5.1動態Dynamic

import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt# torch.manual_seed(1)    # reproducible# Hyper Parameters
INPUT_SIZE = 1          # rnn input size / image width
LR = 0.02               # learning rateclass RNN(nn.Module):def __init__(self):super(RNN, self).__init__()self.rnn = nn.RNN(input_size=1,hidden_size=32,     # rnn hidden unitnum_layers=1,       # number of rnn layerbatch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size))self.out = nn.Linear(32, 1)def forward(self, x, h_state):# x (batch, time_step, input_size)# h_state (n_layers, batch, hidden_size)# r_out (batch, time_step, output_size)r_out, h_state = self.rnn(x, h_state)outs = []                                   # this is where you can find torch is dynamicfor time_step in range(r_out.size(1)):      # calculate output for each time stepouts.append(self.out(r_out[:, time_step, :]))return torch.stack(outs, dim=1), h_staternn = RNN()
print(rnn)optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.MSELoss()                                # the target label is not one-hottedh_state = None   # for initial hidden stateplt.figure(1, figsize=(12, 5))
plt.ion()   # continuously plot########################  Below is different ######################################### static time steps ##########
# for step in range(60):
#     start, end = step * np.pi, (step+1)*np.pi   # time steps
#     # use sin predicts cos
#     steps = np.linspace(start, end, 10, dtype=np.float32)################ dynamic time steps #########
step = 0
for i in range(60):dynamic_steps = np.random.randint(1, 4)  # has random time stepsstart, end = step * np.pi, (step + dynamic_steps) * np.pi  # different time steps lengthstep += dynamic_steps# use sin predicts cossteps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32)#######################  Above is different ###########################print(len(steps))       # print how many time step feed to RNNx_np = np.sin(steps)    # float32 for converting torch FloatTensory_np = np.cos(steps)x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])prediction, h_state = rnn(x, h_state)   # rnn output# !! next step is important !!h_state = h_state.data        # repack the hidden state, break the connection from last iterationloss = loss_func(prediction, y)         # cross entropy lossoptimizer.zero_grad()                   # clear gradients for this training steploss.backward()                         # backpropagation, compute gradientsoptimizer.step()                        # apply gradients# plottingplt.plot(steps, y_np.flatten(), 'r-')plt.plot(steps, prediction.data.numpy().flatten(), 'b-')plt.draw()plt.pause(0.05)plt.ioff()
plt.show()

5.3過擬合Dropout

import torch
import matplotlib.pyplot as plt# torch.manual_seed(1)    # reproducibleN_SAMPLES = 20
N_HIDDEN = 300# training data
x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))# test data
test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))# show data
plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')
plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')
plt.legend(loc='upper left')
plt.ylim((-2.5, 2.5))
plt.show()net_overfitting = torch.nn.Sequential(torch.nn.Linear(1, N_HIDDEN),torch.nn.ReLU(),torch.nn.Linear(N_HIDDEN, N_HIDDEN),torch.nn.ReLU(),torch.nn.Linear(N_HIDDEN, 1),
)net_dropped = torch.nn.Sequential(torch.nn.Linear(1, N_HIDDEN),torch.nn.Dropout(0.5),  # drop 50% of the neurontorch.nn.ReLU(),torch.nn.Linear(N_HIDDEN, N_HIDDEN),torch.nn.Dropout(0.5),  # drop 50% of the neurontorch.nn.ReLU(),torch.nn.Linear(N_HIDDEN, 1),
)print(net_overfitting)  # net architecture
print(net_dropped)optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01)
optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)
loss_func = torch.nn.MSELoss()plt.ion()   # something about plottingfor t in range(500):pred_ofit = net_overfitting(x)pred_drop = net_dropped(x)loss_ofit = loss_func(pred_ofit, y)loss_drop = loss_func(pred_drop, y)optimizer_ofit.zero_grad()optimizer_drop.zero_grad()loss_ofit.backward()loss_drop.backward()optimizer_ofit.step()optimizer_drop.step()if t % 10 == 0:# change to eval mode in order to fix drop out effectnet_overfitting.eval()net_dropped.eval()# parameters for dropout differ from train mode一旦我們用測試集進行結果測試的時候，一定要使用net.eval()把dropout關掉# plottingplt.cla()test_pred_ofit = net_overfitting(test_x)test_pred_drop = net_dropped(test_x)plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train')plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test')plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data.numpy(), fontdict={'size': 20, 'color':  'red'})plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(), fontdict={'size': 20, 'color': 'blue'})plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)# change back to train modenet_overfitting.train()net_dropped.train()plt.ioff()
plt.show()

5.4批標準化

import torch
from torch import nn
from torch.nn import init
import torch.utils.data as Data
import matplotlib.pyplot as plt
import numpy as np# torch.manual_seed(1)    # reproducible
# np.random.seed(1)# Hyper parameters
N_SAMPLES = 2000
BATCH_SIZE = 64
EPOCH = 12
LR = 0.03
N_HIDDEN = 8
ACTIVATION = torch.tanh
B_INIT = -0.2   # use a bad bias constant initializer# training data
x = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis]
noise = np.random.normal(0, 2, x.shape)
y = np.square(x) - 5 + noise# test data
test_x = np.linspace(-7, 10, 200)[:, np.newaxis]
noise = np.random.normal(0, 2, test_x.shape)
test_y = np.square(test_x) - 5 + noisetrain_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()
test_x = torch.from_numpy(test_x).float()
test_y = torch.from_numpy(test_y).float()train_dataset = Data.TensorDataset(train_x, train_y)
train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)# show data
plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')
plt.legend(loc='upper left')class Net(nn.Module):def __init__(self, batch_normalization=False):super(Net, self).__init__()self.do_bn = batch_normalizationself.fcs = []self.bns = []self.bn_input = nn.BatchNorm1d(1, momentum=0.5)   # for input datafor i in range(N_HIDDEN):               # build hidden layers and BN layersinput_size = 1 if i == 0 else 10fc = nn.Linear(input_size, 10)setattr(self, 'fc%i' % i, fc)       # IMPORTANT set layer to the Moduleself._set_init(fc)                  # parameters initializationself.fcs.append(fc)if self.do_bn:bn = nn.BatchNorm1d(10, momentum=0.5)setattr(self, 'bn%i' % i, bn)   # IMPORTANT set layer to the Moduleself.bns.append(bn)self.predict = nn.Linear(10, 1)         # output layerself._set_init(self.predict)            # parameters initializationdef _set_init(self, layer):init.normal_(layer.weight, mean=0., std=.1)init.constant_(layer.bias, B_INIT)def forward(self, x):pre_activation = [x]if self.do_bn: x = self.bn_input(x)     # input batch normalizationlayer_input = [x]for i in range(N_HIDDEN):x = self.fcs[i](x)pre_activation.append(x)if self.do_bn: x = self.bns[i](x)   # batch normalizationx = ACTIVATION(x)layer_input.append(x)out = self.predict(x)return out, layer_input, pre_activationnets = [Net(batch_normalization=False), Net(batch_normalization=True)]# print(*nets)    # print net architectureopts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]loss_func = torch.nn.MSELoss()def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):[a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]if i == 0:p_range = (-7, 10);the_range = (-7, 10)else:p_range = (-4, 4);the_range = (-1, 1)ax_pa.set_title('L' + str(i))ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')plt.pause(0.01)if __name__ == "__main__":f, axs = plt.subplots(4, N_HIDDEN + 1, figsize=(10, 5))plt.ion()  # something about plottingplt.show()# traininglosses = [[], []]  # recode loss for two networksfor epoch in range(EPOCH):print('Epoch: ', epoch)layer_inputs, pre_acts = [], []for net, l in zip(nets, losses):net.eval()              # set eval mode to fix moving_mean and moving_varpred, layer_input, pre_act = net(test_x)l.append(loss_func(pred, test_y).data.item())layer_inputs.append(layer_input)pre_acts.append(pre_act)net.train()             # free moving_mean and moving_varplot_histogram(*layer_inputs, *pre_acts)     # plot histogramfor step, (b_x, b_y) in enumerate(train_loader):for net, opt in zip(nets, opts):     # train for each networkpred, _, _ = net(b_x)loss = loss_func(pred, b_y)opt.zero_grad()loss.backward()opt.step()    # it will also learns the parameters in Batch Normalizationplt.ioff()# plot training lossplt.figure(2)plt.plot(losses[0], c='#FF9359', lw=3, label='Original')plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')# evaluation# set net to eval mode to freeze the parameters in batch normalization layers[net.eval() for net in nets]    # set eval mode to fix moving_mean and moving_varpreds = [net(test_x)[0] for net in nets]plt.figure(3)plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original')plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization')plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train')plt.legend(loc='best')plt.show()