triggle rebuild

utils.py

@@ -35,7 +35,7 @@ class DataLoader(object):
 
         for i in range(n//self.batch_size):
             if self.transform is not None:
-                yield self.transform(X[i*self.batch_size:(i+1)*self.batch_size], 
+                yield self.transform(X[i*self.batch_size:(i+1)*self.batch_size],
                                      y[i*self.batch_size:(i+1)*self.batch_size])
             else:
                 yield (X[i*self.batch_size:(i+1)*self.batch_size],
@@ -59,7 +59,7 @@ def load_data_fashion_mnist(batch_size, resize=None, root="~/.mxnet/datasets/fas
 
     mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=None)
     mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=None)
-    # Transform later to avoid memory explosion. 
+    # Transform later to avoid memory explosion.
     train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform=transform_mnist)
     test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform=transform_mnist)
     return (train_data, test_data)
@@ -129,7 +129,7 @@ def evaluate_accuracy(data_iter, net, ctx=[mx.cpu()]):
             y = y.astype('float32')
             acc += (net(X).argmax(axis=1)==y).sum().copyto(mx.cpu())
             n += y.size
-        acc.wait_to_read() 
+        acc.wait_to_read()
     return acc.asscalar() / n
 
 
@@ -221,7 +221,7 @@ def show_images(imgs, nrows, ncols, figsize=None):
 
 def data_iter_random(corpus_indices, batch_size, num_steps, ctx=None):
     """Sample mini-batches in a random order from sequential data."""
-    # Subtract 1 because label indices are corresponding input indices + 1. 
+    # Subtract 1 because label indices are corresponding input indices + 1.
     num_examples = (len(corpus_indices) - 1) // num_steps
     epoch_size = num_examples // batch_size
     # Randomize samples.
@@ -246,12 +246,12 @@ def data_iter_consecutive(corpus_indices, batch_size, num_steps, ctx=None):
     corpus_indices = nd.array(corpus_indices, ctx=ctx)
     data_len = len(corpus_indices)
     batch_len = data_len // batch_size
-    
+
     indices = corpus_indices[0: batch_size * batch_len].reshape((
         batch_size, batch_len))
-    # Subtract 1 because label indices are corresponding input indices + 1. 
+    # Subtract 1 because label indices are corresponding input indices + 1.
     epoch_size = (batch_len - 1) // num_steps
-    
+
     for i in range(epoch_size):
         i = i * num_steps
         data = indices[:, i: i + num_steps]
@@ -293,7 +293,7 @@ def predict_rnn(rnn, prefix, num_chars, params, hidden_dim, ctx, idx_to_char,
     return ''.join([idx_to_char[i] for i in output])
 
 
-def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim, 
+def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
                           learning_rate, clipping_norm, batch_size,
                           pred_period, pred_len, seqs, get_params, get_inputs,
                           ctx, corpus_indices, idx_to_char, char_to_idx,
@@ -304,10 +304,10 @@ def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
     else:
         data_iter = data_iter_consecutive
     params = get_params()
-    
+
     softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
 
-    for e in range(1, epochs + 1): 
+    for e in range(1, epochs + 1):
         # If consecutive sampling is used, in the same epoch, the hidden state
         # is initialized only at the beginning of the epoch.
         if not is_random_iter:
@@ -315,7 +315,7 @@ def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
             if is_lstm:
                 state_c = nd.zeros(shape=(batch_size, hidden_dim), ctx=ctx)
         train_loss, num_examples = 0, 0
-        for data, label in data_iter(corpus_indices, batch_size, num_steps, 
+        for data, label in data_iter(corpus_indices, batch_size, num_steps,
                                      ctx):
             # If random sampling is used, the hidden state has to be
             # initialized for each mini-batch.
@@ -327,7 +327,7 @@ def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
                 # outputs shape: (batch_size, vocab_size)
                 if is_lstm:
                     outputs, state_h, state_c = rnn(get_inputs(data), state_h,
-                                                    state_c, *params) 
+                                                    state_c, *params)
                 else:
                     outputs, state_h = rnn(get_inputs(data), state_h, *params)
                 # Let t_ib_j be the j-th element of the mini-batch at time i.
@@ -348,7 +348,7 @@ def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
             num_examples += loss.size
 
         if e % pred_period == 0:
-            print("Epoch %d. Training perplexity %f" % (e, 
+            print("Epoch %d. Training perplexity %f" % (e,
                                                exp(train_loss/num_examples)))
             for seq in seqs:
                 print(' - ', predict_rnn(rnn, seq, pred_len, params,
@@ -361,7 +361,7 @@ def set_fig_size(mpl, figsize=(3.5, 2.5)):
     mpl.rcParams['figure.figsize'] = figsize
 
 
-def data_iter(batch_size, num_examples, features, labels): 
+def data_iter(batch_size, num_examples, features, labels):
     """遍历数据集。"""
     indices = list(range(num_examples))
     random.shuffle(indices)
@@ -387,7 +387,7 @@ def optimize(batch_size, trainer, num_epochs, decay_epoch, log_interval,
     data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)
     loss = gloss.L2Loss()
     ls = [loss(net(features), labels).mean().asnumpy()]
-    for epoch in range(1, num_epochs + 1): 
+    for epoch in range(1, num_epochs + 1):
         # 学习率自我衰减。
         if decay_epoch and epoch > decay_epoch:
             trainer.set_learning_rate(trainer.learning_rate * 0.1)
@@ -405,11 +405,9 @@ def optimize(batch_size, trainer, num_epochs, decay_epoch, log_interval,
 
 
 def semilogy(x_vals, y_vals, x_label, y_label, figsize=(3.5, 2.5)):
-    """绘图（y取对数）。"""		
+    """绘图（y取对数）。"""
     set_fig_size(mpl, figsize)
     plt.semilogy(x_vals, y_vals)
     plt.xlabel(x_label)
     plt.ylabel(y_label)
     plt.show()
-
-