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densnet100-cifar10.py

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+import tensorlayer as tl
+from Densnet import *
+import numpy as np
+from tensorlayer.dataflow import Dataset
+
+X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3))
+def generator_train():
+    inputs = X_train
+    targets = y_train
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        yield (_input, np.array(_target))
+
+model = dnet100()
+n_epoch = 50
+batch_size = 128
+print_freq = 2
+shuffle_buffer_size = 128
+train_weights = model.trainable_weights
+optimizer = tl.optimizers.Momentum(0.05, 0.9)
+train_ds = tl.dataflow.FromGenerator(
+    generator_train, output_types=(tl.float32, tl.int32) , column_names=['data', 'label']
+)
+train_ds = tl.dataflow.Shuffle(train_ds,shuffle_buffer_size)
+train_ds = tl.dataflow.Batch(train_ds,batch_size)
+
+
+optimizer = tl.optimizers.Momentum(0.05, 0.9)
+model = tl.models.Model(network=model, loss_fn=tl.cost.softmax_cross_entropy_with_logits, optimizer=optimizer)
+model.train(n_epoch=500, train_dataset=train_ds, print_freq=2)