Run another model (autoencoder) as part of the preprocessing using tf.data.Dataset
0
I'm trying to train a classifier and, as part of the classifier's preprocessing pipeline, I want to run yet another model which is an autoencoder.
I'm using tf.data.Dataset to preprocess my data and compute the classifier's input. My script is equivalent to this
def patch_fn(image, label):
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(10)(x)
model = keras.models.Model(x, y) # This is the autoencoder
encoded_y = model.predict(image, steps=1)
return encoded_y, label
def input_fn():
dataset = tf.data.TFRecordDataset(...)
dataset = dataset.map(patch_fn)
dataset = ...
def main():
classifier = ...
classifier.fit(input_fn(...), ...)
But when I run it, I get
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'dense/MatMul/ReadVariableOp/Placeholder' with dtype resource
[[{{node dense/MatMul/ReadVariableOp/Placeholder}} = Placeholder[dtype=DT_RESOURCE, shape=, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[{{node dense/BiasAdd/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_13_dense/BiasAdd", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Anyone care to point me in the right direction? Thank you.
tensorflow keras tensorflow-datasets
asked Nov 13 at 14:51
Vitor Pereira
3216
add a comment |
0
I'm trying to train a classifier and, as part of the classifier's preprocessing pipeline, I want to run yet another model which is an autoencoder.
I'm using tf.data.Dataset to preprocess my data and compute the classifier's input. My script is equivalent to this
def patch_fn(image, label):
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(10)(x)
model = keras.models.Model(x, y) # This is the autoencoder
encoded_y = model.predict(image, steps=1)
return encoded_y, label
def input_fn():
dataset = tf.data.TFRecordDataset(...)
dataset = dataset.map(patch_fn)
dataset = ...
def main():
classifier = ...
classifier.fit(input_fn(...), ...)
But when I run it, I get
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'dense/MatMul/ReadVariableOp/Placeholder' with dtype resource
[[{{node dense/MatMul/ReadVariableOp/Placeholder}} = Placeholder[dtype=DT_RESOURCE, shape=, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[{{node dense/BiasAdd/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_13_dense/BiasAdd", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Anyone care to point me in the right direction? Thank you.
tensorflow keras tensorflow-datasets
asked Nov 13 at 14:51
Vitor Pereira
3216
add a comment |
0
0
0
I'm trying to train a classifier and, as part of the classifier's preprocessing pipeline, I want to run yet another model which is an autoencoder.
I'm using tf.data.Dataset to preprocess my data and compute the classifier's input. My script is equivalent to this
def patch_fn(image, label):
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(10)(x)
model = keras.models.Model(x, y) # This is the autoencoder
encoded_y = model.predict(image, steps=1)
return encoded_y, label
def input_fn():
dataset = tf.data.TFRecordDataset(...)
dataset = dataset.map(patch_fn)
dataset = ...
def main():
classifier = ...
classifier.fit(input_fn(...), ...)
But when I run it, I get
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'dense/MatMul/ReadVariableOp/Placeholder' with dtype resource
[[{{node dense/MatMul/ReadVariableOp/Placeholder}} = Placeholder[dtype=DT_RESOURCE, shape=, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[{{node dense/BiasAdd/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_13_dense/BiasAdd", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Anyone care to point me in the right direction? Thank you.
tensorflow keras tensorflow-datasets
asked Nov 13 at 14:51
Vitor Pereira
3216
I'm trying to train a classifier and, as part of the classifier's preprocessing pipeline, I want to run yet another model which is an autoencoder.
I'm using tf.data.Dataset to preprocess my data and compute the classifier's input. My script is equivalent to this
def patch_fn(image, label):
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(10)(x)
model = keras.models.Model(x, y) # This is the autoencoder
encoded_y = model.predict(image, steps=1)
return encoded_y, label
def input_fn():
dataset = tf.data.TFRecordDataset(...)
dataset = dataset.map(patch_fn)
dataset = ...
def main():
classifier = ...
classifier.fit(input_fn(...), ...)
But when I run it, I get
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'dense/MatMul/ReadVariableOp/Placeholder' with dtype resource
[[{{node dense/MatMul/ReadVariableOp/Placeholder}} = Placeholder[dtype=DT_RESOURCE, shape=, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[{{node dense/BiasAdd/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_13_dense/BiasAdd", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Anyone care to point me in the right direction? Thank you.
tensorflow keras tensorflow-datasets
tensorflow keras tensorflow-datasets
asked Nov 13 at 14:51
Vitor Pereira
3216
asked Nov 13 at 14:51
Vitor Pereira
3216
asked Nov 13 at 14:51
Vitor Pereira
3216
asked Nov 13 at 14:51
Vitor Pereira
3216
asked Nov 13 at 14:51
Vitor Pereira
3216
3216
add a comment |
add a comment |
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