placeholder->TensorSpec refactor

docs/tutorial/training-interface.md

@@ -17,7 +17,7 @@ expects 4 arguments to setup the graph: `InputDesc`, `InputSource`, get_cost fun
 ```python
 class MyModel(ModelDesc):
   def inputs(self):
-    return [tf.placeholder(dtype, shape, name), tf.placeholder(dtype, shape, name), ... ]
+    return [tf.TensorSpec(shape, dtype, name), tf.TensorSpec(shape, dtype, name), ... ]
 
   def build_graph(self, tensorA, tensorB, ...):  # inputs
     # build the graph

examples/A3C-Gym/train-atari.py

@@ -69,10 +69,10 @@ class MySimulatorWorker(SimulatorProcess):
 class Model(ModelDesc):
     def inputs(self):
         assert NUM_ACTIONS is not None
-        return [tf.placeholder(tf.uint8, (None,) + STATE_SHAPE + (FRAME_HISTORY, ), 'state'),
-                tf.placeholder(tf.int64, (None,), 'action'),
-                tf.placeholder(tf.float32, (None,), 'futurereward'),
-                tf.placeholder(tf.float32, (None,), 'action_prob'),
+        return [tf.TensorSpec((None,) + STATE_SHAPE + (FRAME_HISTORY, ), tf.uint8, 'state'),
+                tf.TensorSpec((None,), tf.int64, 'action'),
+                tf.TensorSpec((None,), tf.float32, 'futurereward'),
+                tf.TensorSpec((None,), tf.float32, 'action_prob'),
                 ]
 
     def _get_NN_prediction(self, state):

examples/CTC-TIMIT/train-timit.py

@@ -26,11 +26,11 @@ FEATUREDIM = 39     # MFCC feature dimension
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, None, FEATUREDIM], 'feat'),   # bxmaxseqx39
-                tf.placeholder(tf.int64, [None, None], 'labelidx'),  # label is b x maxlen, sparse
-                tf.placeholder(tf.int32, [None], 'labelvalue'),
-                tf.placeholder(tf.int64, [None], 'labelshape'),
-                tf.placeholder(tf.int32, [None], 'seqlen'),   # b
+        return [tf.TensorSpec([None, None, FEATUREDIM], tf.float32, 'feat'),   # bxmaxseqx39
+                tf.TensorSpec([None, None], tf.int64, 'labelidx'),  # label is b x maxlen, sparse
+                tf.TensorSpec([None], tf.int32, 'labelvalue'),
+                tf.TensorSpec([None], tf.int64, 'labelshape'),
+                tf.TensorSpec([None], tf.int32, 'seqlen'),   # b
                 ]
 
     def build_graph(self, feat, labelidx, labelvalue, labelshape, seqlen):

examples/Char-RNN/char-rnn.py

@@ -70,8 +70,8 @@ class CharRNNData(RNGDataFlow):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.int32, (None, param.seq_len), 'input'),
-                tf.placeholder(tf.int32, (None, param.seq_len), 'nextinput')]
+        return [tf.TensorSpec((None, param.seq_len), tf.int32, 'input'),
+                tf.TensorSpec((None, param.seq_len), tf.int32, 'nextinput')]
 
     def build_graph(self, input, nextinput):
         cell = rnn.MultiRNNCell([rnn.LSTMBlockCell(num_units=param.rnn_size)

examples/DeepQNetwork/DQNModel.py

@@ -34,12 +34,10 @@ class Model(ModelDesc):
         # When we use h history frames, the current state and the next state will have (h-1) overlapping frames.
         # Therefore we use a combined state for efficiency:
         # The first h are the current state, and the last h are the next state.
-        return [tf.placeholder(self.state_dtype,
-                               (None,) + self.state_shape + (self.history + 1, ),
-                               'comb_state'),
-                tf.placeholder(tf.int64, (None,), 'action'),
-                tf.placeholder(tf.float32, (None,), 'reward'),
-                tf.placeholder(tf.bool, (None,), 'isOver')]
+        return [tf.TensorSpec((None,) + self.state_shape + (self.history + 1, ), self.state_dtype, 'comb_state'),
+                tf.TensorSpec((None,), tf.int64, 'action'),
+                tf.TensorSpec((None,), tf.float32, 'reward'),
+                tf.TensorSpec((None,), tf.bool, 'isOver')]
 
     @abc.abstractmethod
     def _get_DQN_prediction(self, state):

examples/DoReFa-Net/resnet-dorefa.py

@@ -33,8 +33,8 @@ BITG = 32
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 224, 224, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, 224, 224, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = image / 256.0

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -41,8 +41,8 @@ BITG = 4
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 40, 40, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, 40, 40, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         is_training = get_current_tower_context().is_training

examples/DynamicFilterNetwork/steering-filter.py

@@ -95,10 +95,10 @@ class OnlineTensorboardExport(Callback):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (BATCH, ), 'theta'),
-                tf.placeholder(tf.float32, (BATCH, SHAPE, SHAPE), 'image'),
-                tf.placeholder(tf.float32, (BATCH, SHAPE, SHAPE), 'gt_image'),
-                tf.placeholder(tf.float32, (BATCH, 9, 9), 'gt_filter')]
+        return [tf.TensorSpec((BATCH, ), tf.float32, 'theta'),
+                tf.TensorSpec((BATCH, SHAPE, SHAPE), tf.float32, 'image'),
+                tf.TensorSpec((BATCH, SHAPE, SHAPE), tf.float32, 'gt_image'),
+                tf.TensorSpec((BATCH, 9, 9), tf.float32, 'gt_filter')]
 
     def _parameter_net(self, theta, kernel_shape=9):
         """Estimate filters for convolution layers

examples/FasterRCNN/train.py

@@ -98,14 +98,14 @@ class DetectionModel(ModelDesc):
 class ResNetC4Model(DetectionModel):
     def inputs(self):
         ret = [
-            tf.placeholder(tf.float32, (None, None, 3), 'image'),
-            tf.placeholder(tf.int32, (None, None, cfg.RPN.NUM_ANCHOR), 'anchor_labels'),
-            tf.placeholder(tf.float32, (None, None, cfg.RPN.NUM_ANCHOR, 4), 'anchor_boxes'),
-            tf.placeholder(tf.float32, (None, 4), 'gt_boxes'),
-            tf.placeholder(tf.int64, (None,), 'gt_labels')]  # all > 0
+            tf.TensorSpec((None, None, 3), tf.float32, 'image'),
+            tf.TensorSpec((None, None, cfg.RPN.NUM_ANCHOR), tf.int32, 'anchor_labels'),
+            tf.TensorSpec((None, None, cfg.RPN.NUM_ANCHOR, 4), tf.float32, 'anchor_boxes'),
+            tf.TensorSpec((None, 4), tf.float32, 'gt_boxes'),
+            tf.TensorSpec((None,), tf.int64, 'gt_labels')]  # all > 0
         if cfg.MODE_MASK:
             ret.append(
-                tf.placeholder(tf.uint8, (None, None, None), 'gt_masks')
+                tf.TensorSpec((None, None, None), tf.uint8, 'gt_masks')
             )   # NR_GT x height x width
         return ret
 
@@ -199,20 +199,20 @@ class ResNetFPNModel(DetectionModel):
 
     def inputs(self):
         ret = [
-            tf.placeholder(tf.float32, (None, None, 3), 'image')]
+            tf.TensorSpec((None, None, 3), tf.float32, 'image')]
         num_anchors = len(cfg.RPN.ANCHOR_RATIOS)
         for k in range(len(cfg.FPN.ANCHOR_STRIDES)):
             ret.extend([
-                tf.placeholder(tf.int32, (None, None, num_anchors),
-                               'anchor_labels_lvl{}'.format(k + 2)),
-                tf.placeholder(tf.float32, (None, None, num_anchors, 4),
-                               'anchor_boxes_lvl{}'.format(k + 2))])
+                tf.TensorSpec((None, None, num_anchors), tf.int32,
+                              'anchor_labels_lvl{}'.format(k + 2)),
+                tf.TensorSpec((None, None, num_anchors, 4), tf.float32,
+                              'anchor_boxes_lvl{}'.format(k + 2))])
         ret.extend([
-            tf.placeholder(tf.float32, (None, 4), 'gt_boxes'),
-            tf.placeholder(tf.int64, (None,), 'gt_labels')])  # all > 0
+            tf.TensorSpec((None, 4), tf.float32, 'gt_boxes'),
+            tf.TensorSpec((None,), tf.int64, 'gt_labels')])  # all > 0
         if cfg.MODE_MASK:
             ret.append(
-                tf.placeholder(tf.uint8, (None, None, None), 'gt_masks')
+                tf.TensorSpec((None, None, None), tf.uint8, 'gt_masks')
             )   # NR_GT x height x width
         return ret
 

examples/GAN/BEGAN.py

@@ -28,7 +28,7 @@ GAMMA = 0.5
 
 class Model(GANModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, args.final_size, args.final_size, 3), 'input')]
+        return [tf.TensorSpec((None, args.final_size, args.final_size, 3), tf.float32, 'input')]
 
     @auto_reuse_variable_scope
     def decoder(self, z):

examples/GAN/ConditionalGAN-mnist.py

@@ -41,8 +41,8 @@ def batch_flatten(x):
 
 class Model(GANModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 28, 28), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, 28, 28), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def generator(self, z, y):
         l = FullyConnected('fc0', tf.concat([z, y], 1), 1024, activation=BNReLU)

examples/GAN/CycleGAN.py

@@ -42,8 +42,8 @@ def INLReLU(x, name=None):
 
 class Model(GANModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, SHAPE, SHAPE, 3), 'inputA'),
-                tf.placeholder(tf.float32, (None, SHAPE, SHAPE, 3), 'inputB')]
+        return [tf.TensorSpec((None, SHAPE, SHAPE, 3), tf.float32, 'inputA'),
+                tf.TensorSpec((None, SHAPE, SHAPE, 3), tf.float32, 'inputB')]
 
     @staticmethod
     def build_res_block(x, name, chan, first=False):

examples/GAN/DCGAN.py

@@ -41,7 +41,7 @@ class Model(GANModelDesc):
         self.zdim = z_dim
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, self.shape, self.shape, 3), 'input')]
+        return [tf.TensorSpec((None, self.shape, self.shape, 3), tf.float32, 'input')]
 
     def generator(self, z):
         """ return an image generated from z"""

examples/GAN/DiscoGAN-CelebA.py

@@ -35,8 +35,8 @@ def BNLReLU(x, name=None):
 
 class Model(GANModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, SHAPE, SHAPE, 3), 'inputA'),
-                tf.placeholder(tf.float32, (None, SHAPE, SHAPE, 3), 'inputB')]
+        return [tf.TensorSpec((None, SHAPE, SHAPE, 3), tf.float32, 'inputA'),
+                tf.TensorSpec((None, SHAPE, SHAPE, 3), tf.float32, 'inputB')]
 
     @auto_reuse_variable_scope
     def generator(self, img):

examples/GAN/Image2Image.py

@@ -63,8 +63,8 @@ def visualize_tensors(name, imgs, scale_func=lambda x: (x + 1.) * 128., max_outp
 class Model(GANModelDesc):
     def inputs(self):
         SHAPE = 256
-        return [tf.placeholder(tf.float32, (None, SHAPE, SHAPE, IN_CH), 'input'),
-                tf.placeholder(tf.float32, (None, SHAPE, SHAPE, OUT_CH), 'output')]
+        return [tf.TensorSpec((None, SHAPE, SHAPE, IN_CH), tf.float32, 'input'),
+                tf.TensorSpec((None, SHAPE, SHAPE, OUT_CH), tf.float32, 'output')]
 
     def generator(self, imgs):
         # imgs: input: 256x256xch

examples/GAN/InfoGAN-mnist.py

@@ -106,7 +106,7 @@ def sample_prior(batch_size):
 
 class Model(GANModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 28, 28), 'input')]
+        return [tf.TensorSpec((None, 28, 28), tf.float32, 'input')]
 
     def generator(self, z):
         l = FullyConnected('fc0', z, 1024, activation=BNReLU)

examples/HED/hed.py

@@ -103,8 +103,8 @@ def CaffeBilinearUpSample(x, shape):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, None, None, 3], 'image'),
-                tf.placeholder(tf.int32, [None, None, None], 'edgemap')]
+        return [tf.TensorSpec([None, None, None, 3], tf.float32, 'image'),
+                tf.TensorSpec([None, None, None], tf.int32, 'edgemap')]
 
     def build_graph(self, image, edgemap):
         image = image - tf.constant([104, 116, 122], dtype='float32')

examples/ImageNetModels/imagenet_utils.py

@@ -324,8 +324,8 @@ class ImageNetModel(ModelDesc):
     label_smoothing = 0.
 
     def inputs(self):
-        return [tf.placeholder(self.image_dtype, [None, self.image_shape, self.image_shape, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, self.image_shape, self.image_shape, 3], self.image_dtype, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = self.image_preprocess(image)

examples/ImageNetModels/inception-bn.py

@@ -23,8 +23,8 @@ INPUT_SHAPE = 224
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, INPUT_SHAPE, INPUT_SHAPE, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = image / 128.0

examples/OpticalFlow/flownet_models.py

@@ -145,9 +145,9 @@ class FlowNetBase(ModelDesc):
         self.width = width
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (1, 3, self.height, self.width), 'left'),
-                tf.placeholder(tf.float32, (1, 3, self.height, self.width), 'right'),
-                tf.placeholder(tf.float32, (1, 2, self.height, self.width), 'gt_flow')]
+        return [tf.TensorSpec((1, 3, self.height, self.width), tf.float32, 'left'),
+                tf.TensorSpec((1, 3, self.height, self.width), tf.float32, 'right'),
+                tf.TensorSpec((1, 2, self.height, self.width), tf.float32, 'gt_flow')]
 
     def graph_structure(self, inputs):
         """

examples/PennTreebank/PTB-LSTM.py

@@ -46,8 +46,8 @@ def get_PennTreeBank(data_dir=None):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.int32, (None, SEQ_LEN), 'input'),
-                tf.placeholder(tf.int32, (None, SEQ_LEN), 'nextinput')]
+        return [tf.TensorSpec((None, SEQ_LEN), tf.int32, 'input'),
+                tf.TensorSpec((None, SEQ_LEN), tf.int32, 'nextinput')]
 
     def build_graph(self, input, nextinput):
         is_training = get_current_tower_context().is_training

examples/ResNet/cifar10-preact18-mixup.py

@@ -40,8 +40,8 @@ def preactivation_block(input, num_filters, stride=1):
 
 class ResNet_Cifar(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 32, 32, 3], 'input'),
-                tf.placeholder(tf.float32, [None, CLASS_NUM], 'label')]
+        return [tf.TensorSpec([None, 32, 32, 3], tf.float32, 'input'),
+                tf.TensorSpec([None, CLASS_NUM], tf.float32, 'label')]
 
     def build_graph(self, image, label):
         assert tf.test.is_gpu_available()

examples/ResNet/cifar10-resnet.py

@@ -39,8 +39,8 @@ class Model(ModelDesc):
         self.n = n
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 32, 32, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, 32, 32, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = image / 128.0

examples/ResNet/load-resnet.py

@@ -29,8 +29,8 @@ CFG = {
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 224, 224, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, 224, 224, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         blocks = CFG[DEPTH]

examples/Saliency/saliency-maps.py

@@ -54,7 +54,7 @@ def saliency_map(output, input, name="saliency_map"):
 
 class Model(tp.ModelDescBase):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (IMAGE_SIZE, IMAGE_SIZE, 3), 'image')]
+        return [tf.TensorSpec((IMAGE_SIZE, IMAGE_SIZE, 3), tf.float32, 'image')]
 
     def build_graph(self, orig_image):
         mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])

examples/SimilarityLearning/mnist-embeddings.py

@@ -236,9 +236,9 @@ class SiameseModel(EmbeddingModel):
         return ds
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 28, 28), 'input'),
-                tf.placeholder(tf.float32, (None, 28, 28), 'input_y'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, 28, 28), tf.float32, 'input'),
+                tf.TensorSpec((None, 28, 28), tf.float32, 'input_y'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, x, y, label):
         # embed them
@@ -280,9 +280,9 @@ class TripletModel(EmbeddingModel):
         return ds
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 28, 28), 'input'),
-                tf.placeholder(tf.float32, (None, 28, 28), 'input_p'),
-                tf.placeholder(tf.float32, (None, 28, 28), 'input_n')]
+        return [tf.TensorSpec((None, 28, 28), tf.float32, 'input'),
+                tf.TensorSpec((None, 28, 28), tf.float32, 'input_p'),
+                tf.TensorSpec((None, 28, 28), tf.float32, 'input_n')]
 
     def loss(self, a, p, n):
         return triplet_loss(a, p, n, 5., extra=True, scope="loss")
@@ -314,8 +314,8 @@ class CenterModel(EmbeddingModel):
         return ds
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 28, 28), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, 28, 28), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, x, label):
         # embed them

examples/SpatialTransformer/mnist-addition.py

@@ -107,8 +107,8 @@ def GridSample(inputs, borderMode='repeat'):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE, 2), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, IMAGE_SIZE, IMAGE_SIZE, 2), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         xys = np.array([(y, x, 1) for y in range(WARP_TARGET_SIZE)

examples/SuperResolution/enet-pat.py

@@ -50,8 +50,8 @@ class Model(GANModelDesc):
         self.width = width
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, self.height * 1, self.width * 1, CHANNELS), 'Ilr'),
-                tf.placeholder(tf.float32, (None, self.height * 4, self.width * 4, CHANNELS), 'Ihr')]
+        return [tf.TensorSpec((None, self.height * 1, self.width * 1, CHANNELS), tf.float32, 'Ilr'),
+                tf.TensorSpec((None, self.height * 4, self.width * 4, CHANNELS), tf.float32, 'Ihr')]
 
     def build_graph(self, Ilr, Ihr):
         Ilr, Ihr = Ilr / 255.0, Ihr / 255.0

examples/basics/cifar-convnet.py

@@ -29,8 +29,8 @@ class Model(ModelDesc):
         self.cifar_classnum = cifar_classnum
 
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, 30, 30, 3), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, 30, 30, 3), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         is_training = get_current_tower_context().is_training

examples/basics/export-model.py

@@ -44,8 +44,8 @@ class Model(ModelDesc):
     """
 
     def inputs(self):
-        return [tf.placeholder(tf.uint8, (None, SHAPE, SHAPE, CHANNELS), 'input_img'),
-                tf.placeholder(tf.uint8, (None, SHAPE, SHAPE, CHANNELS), 'target_img')]
+        return [tf.TensorSpec((None, SHAPE, SHAPE, CHANNELS), tf.uint8, 'input_img'),
+                tf.TensorSpec((None, SHAPE, SHAPE, CHANNELS), tf.uint8, 'target_img')]
 
     def make_prediction(self, img):
 
@@ -86,7 +86,7 @@ class InferenceOnlyModel(Model):
 
     def inputs(self):
         # The inference graph only accepts a single image, which is different to the training model.
-        return [tf.placeholder(tf.string, (None,), 'input_img_bytes')]
+        return [tf.TensorSpec((None,), tf.string, 'input_img_bytes')]
 
     def build_graph(self, input_img_bytes):
         # prepare input (png encoded strings to images)

examples/basics/mnist-tflayers.py

@@ -26,8 +26,8 @@ class Model(ModelDesc):
         """
         Define all the inputs (with type, shape, name) that the graph will need.
         """
-        return [tf.placeholder(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, IMAGE_SIZE, IMAGE_SIZE), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         """This function should build the model which takes the input variables

examples/basics/mnist-tfslim.py

@@ -22,8 +22,8 @@ IMAGE_SIZE = 28
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, IMAGE_SIZE, IMAGE_SIZE), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = tf.expand_dims(image, 3)

examples/basics/mnist-visualizations.py

@@ -66,8 +66,8 @@ def visualize_conv_activations(activation, name):
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, IMAGE_SIZE, IMAGE_SIZE), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = tf.expand_dims(image * 2 - 1, 3)

examples/basics/svhn-digit-convnet.py

@@ -22,8 +22,8 @@ Each epoch iterates over the whole training set (4721 iterations), and takes abo
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 40, 40, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
+        return [tf.TensorSpec([None, 40, 40, 3], tf.float32, 'input'),
+                tf.TensorSpec([None], tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = image / 128.0 - 1

examples/boilerplate.py

@@ -19,8 +19,8 @@ CHANNELS = 3
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, SHAPE, SHAPE, CHANNELS), 'input1'),
-                tf.placeholder(tf.int32, (None,), 'input2')]
+        return [tf.TensorSpec((None, SHAPE, SHAPE, CHANNELS), tf.float32, 'input1'),
+                tf.TensorSpec((None,), tf.int32, 'input2')]
 
     def build_graph(self, input1, input2):
 

examples/keras/mnist-keras.py

@@ -41,8 +41,8 @@ def get_keras_model():
 
 class Model(ModelDesc):
     def inputs(self):
-        return [tf.placeholder(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                tf.placeholder(tf.int32, (None,), 'label')]
+        return [tf.TensorSpec((None, IMAGE_SIZE, IMAGE_SIZE), tf.float32, 'input'),
+                tf.TensorSpec((None,), tf.int32, 'label')]
 
     def build_graph(self, image, label):
         image = tf.expand_dims(image, 3) * 2 - 1