resnet small

resnet_small.py

@graph_callable.graph_callable([])
def resnet_loss():
 “””Resnet loss from random input”””
 network = resnet_model.cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES)
 inputs = tf.reshape(images, [BATCH_SIZE, HEIGHT, WIDTH, DEPTH])
 logits = network(inputs,True)
 cross_entropy = tf.losses.softmax_cross_entropy(logits=logits,
 onehot_labels=labels)
 return cross_entropy
loss_and_grads_fn = tfe.implicit_value_and_gradients(resnet_loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
losses = []
for i in range(500):
 loss, grads_and_vars = loss_and_grads_fn()
 optimizer.apply_gradients(grads_and_vars)
 print(loss)
 losses.append(loss.numpy())