update keras example

examples/mnist-keras.py

@@ -10,6 +10,7 @@ import os
 import sys
 import argparse
 
+import keras
 import keras.layers as KL
 import keras.backend as KB
 from keras.models import Sequential
@@ -27,52 +28,45 @@ from tensorpack.utils.argtools import memoized
 IMAGE_SIZE = 28
 
 
+@memoized        # this is necessary for sonnet/Keras to work under tensorpack
+def get_keras_model():
+    M = Sequential()
+    M.add(KL.Conv2D(32, 3, activation='relu', input_shape=[IMAGE_SIZE, IMAGE_SIZE, 1], padding='same'))
+    M.add(KL.MaxPooling2D())
+    M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
+    M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
+    M.add(KL.MaxPooling2D())
+    M.add(KL.Conv2D(32, 3, padding='same', activation='relu'))
+    M.add(KL.Flatten())
+    M.add(KL.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(1e-5)))
+    M.add(KL.Dropout(0.5))
+    M.add(KL.Dense(10, activation=None, kernel_regularizer=regularizers.l2(1e-5)))
+    return M
+
+
 class Model(ModelDesc):
     def _get_inputs(self):
         return [InputDesc(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                InputDesc(tf.int32, (None,), 'label'),
-                ]
-
-    @memoized   # this is necessary for sonnet/Keras to work under tensorpack
-    def _build_keras_model(self):
-        M = Sequential()
-        M.add(KL.Conv2D(32, 3, activation='relu', input_shape=[IMAGE_SIZE, IMAGE_SIZE, 1], padding='same'))
-        M.add(KL.MaxPooling2D())
-        M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
-        M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
-        M.add(KL.MaxPooling2D())
-        M.add(KL.Conv2D(32, 3, padding='same', activation='relu'))
-        M.add(KL.Flatten())
-        M.add(KL.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(1e-5)))
-        M.add(KL.Dropout(0.5))
-        M.add(KL.Dense(10, activation=None, kernel_regularizer=regularizers.l2(1e-5)))
-        return M
+                InputDesc(tf.int32, (None,), 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs
-        image = tf.expand_dims(image, 3)
-
-        image = image * 2 - 1   # center the pixels values at zero
+        image = tf.expand_dims(image, 3) * 2 - 1
 
-        with argscope(Conv2D, kernel_shape=3, nl=tf.nn.relu, out_channel=32):
-            M = self._build_keras_model()
-            logits = M(image)
-        prob = tf.nn.softmax(logits, name='prob')   # a Bx10 with probabilities
-
-        # a vector of length B with loss of each sample
+        M = get_keras_model()
+        logits = M(image)
+        # build cost function by tensorflow
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')  # the average cross-entropy loss
 
+        # for tensorpack validation
         wrong = symbolic_functions.prediction_incorrect(logits, label, name='incorrect')
         train_error = tf.reduce_mean(wrong, name='train_error')
         summary.add_moving_summary(train_error)
 
         wd_cost = tf.add_n(M.losses, name='regularize_loss')    # this is how Keras manage regularizers
         self.cost = tf.add_n([wd_cost, cost], name='total_cost')
-        summary.add_moving_summary(cost, wd_cost, self.cost)
-
-        # this is the keras naming
-        summary.add_param_summary(('conv2d.*/kernel', ['histogram', 'rms']))
+        summary.add_moving_summary(self.cost)
 
     def _get_optimizer(self):
         lr = tf.train.exponential_decay(
@@ -84,7 +78,7 @@ class Model(ModelDesc):
         return tf.train.AdamOptimizer(lr)
 
 
-# Keras needs an extra input
+# Keras needs an extra input if learning_phase is needed
 class KerasCallback(Callback):
     def __init__(self, isTrain):
         self._isTrain = isTrain
@@ -106,31 +100,23 @@ def get_config():
     dataset_train, dataset_test = get_data()
 
     return TrainConfig(
-        model=Model(),
+        model=KerasModel(get_keras_model()),
         dataflow=dataset_train,
         callbacks=[
-            KerasCallback(1),   # for Keras training
+            KerasCallback(True),   # for Keras training
             ModelSaver(),
             InferenceRunner(
                 dataset_test,
                 [ScalarStats('cross_entropy_loss'), ClassificationError('incorrect')],
-                extra_hooks=[CallbackToHook(KerasCallback(0))]),    # for keras inference
+                extra_hooks=[CallbackToHook(KerasCallback(False))]),    # for keras inference
         ],
         max_epoch=100,
     )
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
-    args = parser.parse_args()
-    if args.gpu:
-        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-
     config = get_config()
-    if args.gpu:
-        config.nr_tower = len(args.gpu.split(','))
-    if config.nr_tower > 1:
-        SyncMultiGPUTrainer(config).train()
-    else:
-        QueueInputTrainer(config).train()
+    QueueInputTrainer(config).train()
+    # for multigpu training:
+    # config.nr_tower = 2
+    # SyncMultiGPUTrainer(config).train()