После обучения я сохранил свою модель в формате Saved_model (я хочу сохранить ее в этом формате, а не .h5). При загрузке модели и печати графика я не могу найти входной тензор (только serve_default_input). уметь делать прогноз.
Впервые я определил свою модель с помощью keras.applications.VGG16, а затем добавил keras.Input(), но ничего не изменилось.
Вот как я определил свою модель:
model = keras.applications.VGG16(weights = "imagenet",
include_top = False,
input_shape = (IMG_SIZE[0],IMG_SIZE[1], 3))
for layer in model.layers:
layer.trainable = False
x = model.output
x = Dense(16 , activation="relu")(x)
x = Flatten()(x)
predictions = Dense(1, activation = "sigmoid")(x)
model = Model(inputs = model.input, outputs = predictions)
model.summary() #in the first attempt :
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) [(None, 512, 512, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 512, 512, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 512, 512, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 256, 256, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 256, 256, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 256, 256, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 128, 128, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 128, 128, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 128, 128, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 128, 128, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 64, 64, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 64, 64, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 64, 64, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 64, 64, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 32, 32, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 16, 16, 512) 0
_________________________________________________________________
dense_5 (Dense) (None, 16, 16, 16) 8208
_________________________________________________________________
flatten_3 (Flatten) (None, 4096) 0
_________________________________________________________________
dense_6 (Dense) (None, 1) 4097
=================================================================
Total params: 14,726,993
Trainable params: 12,305
Non-trainable params: 14,714,688
_________________________________________________________________
None
model.summary() #in the second attempt :
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 512, 512, 3)] 0
_________________________________________________________________
vgg16 (Model) (None, 16, 16, 512) 14714688
_________________________________________________________________
dense_3 (Dense) (None, 16, 16, 16) 8208
_________________________________________________________________
flatten_2 (Flatten) (None, 4096) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 4097
=================================================================
Total params: 14,726,993
Trainable params: 12,305
Non-trainable params: 14,714,688
_________________________________________________________________
None
с точки зрения кераса. После преобразования в SavedModel,
tf.reset_default_graph()
graph = tf.Graph()
sess = tf.Session(graph=graph)
tf.saved_model.loader.load(sess, [tf.saved_model.SERVING], "SavedModel")
sess.graph.get_operations()
[<tf.Operation 'dense_3_1/kernel' type=VarHandleOp>,
<tf.Operation 'dense_3_1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'dense_3_1/bias' type=VarHandleOp>,
<tf.Operation 'dense_3_1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'dense_4_1/kernel' type=VarHandleOp>,
<tf.Operation 'dense_4_1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'dense_4_1/bias' type=VarHandleOp>,
<tf.Operation 'dense_4_1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block1_conv1/kernel' type=VarHandleOp>,
<tf.Operation 'block1_conv1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block1_conv1/bias' type=VarHandleOp>,
<tf.Operation 'block1_conv1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block1_conv2/kernel' type=VarHandleOp>,
<tf.Operation 'block1_conv2/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block1_conv2/bias' type=VarHandleOp>,
<tf.Operation 'block1_conv2/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block2_conv1/kernel' type=VarHandleOp>,
<tf.Operation 'block2_conv1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block2_conv1/bias' type=VarHandleOp>,
<tf.Operation 'block2_conv1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block2_conv2/kernel' type=VarHandleOp>,
<tf.Operation 'block2_conv2/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block2_conv2/bias' type=VarHandleOp>,
<tf.Operation 'block2_conv2/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv1/kernel' type=VarHandleOp>,
<tf.Operation 'block3_conv1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv1/bias' type=VarHandleOp>,
<tf.Operation 'block3_conv1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv2/kernel' type=VarHandleOp>,
<tf.Operation 'block3_conv2/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv2/bias' type=VarHandleOp>,
<tf.Operation 'block3_conv2/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv3/kernel' type=VarHandleOp>,
<tf.Operation 'block3_conv3/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block3_conv3/bias' type=VarHandleOp>,
<tf.Operation 'block3_conv3/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv1/kernel' type=VarHandleOp>,
<tf.Operation 'block4_conv1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv1/bias' type=VarHandleOp>,
<tf.Operation 'block4_conv1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv2/kernel' type=VarHandleOp>,
<tf.Operation 'block4_conv2/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv2/bias' type=VarHandleOp>,
<tf.Operation 'block4_conv2/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv3/kernel' type=VarHandleOp>,
<tf.Operation 'block4_conv3/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block4_conv3/bias' type=VarHandleOp>,
<tf.Operation 'block4_conv3/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv1/kernel' type=VarHandleOp>,
<tf.Operation 'block5_conv1/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv1/bias' type=VarHandleOp>,
<tf.Operation 'block5_conv1/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv2/kernel' type=VarHandleOp>,
<tf.Operation 'block5_conv2/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv2/bias' type=VarHandleOp>,
<tf.Operation 'block5_conv2/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv3/kernel' type=VarHandleOp>,
<tf.Operation 'block5_conv3/kernel/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'block5_conv3/bias' type=VarHandleOp>,
<tf.Operation 'block5_conv3/bias/Read/ReadVariableOp' type=ReadVariableOp>,
<tf.Operation 'NoOp' type=NoOp>,
<tf.Operation 'Const' type=Const>,
<tf.Operation 'serving_default_input_5' type=Placeholder>,
<tf.Operation 'StatefulPartitionedCall' type=StatefulPartitionedCall>,
<tf.Operation 'saver_filename' type=Placeholder>,
<tf.Operation 'StatefulPartitionedCall_1' type=StatefulPartitionedCall>,
<tf.Operation 'StatefulPartitionedCall_2' type=StatefulPartitionedCall>]
, поэтому, когда я пытаюсь сделать прогноз:
in_t = sess.graph.get_tensor_by_name('serving_default_input_5:0')
out = sess.graph.get_tensor_by_name('dense_4_1/bias/Read/ReadVariableOp:0')
...
pred = sess.run([out], feed_dict={ in_t: image}) # image has the right shape
Как я могу передать изображение формы (512,512,3) моемузагружен сохраненный_модель?
TY заранее ^^