How to run several times a model in tensorflow?
I want to train several times a file ConvNet.py in order to produce some statistics about its training like precision, confussion matrices, etc. So, I tried (in google colab) to do something like
for k in range(10:
%run ConvNet.py
The first training goes well, but when in begin the second, arise a problem. It says that "weights variable already defined, disallowed" (weights is the fist variable that I define in ConvNet.py) and the script stops.
I tried clearing variables with os kill, but there is still problems. How can I fix this?
python tensorflow google-colaboratory
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
add a comment |
I want to train several times a file ConvNet.py in order to produce some statistics about its training like precision, confussion matrices, etc. So, I tried (in google colab) to do something like
for k in range(10:
%run ConvNet.py
The first training goes well, but when in begin the second, arise a problem. It says that "weights variable already defined, disallowed" (weights is the fist variable that I define in ConvNet.py) and the script stops.
I tried clearing variables with os kill, but there is still problems. How can I fix this?
python tensorflow google-colaboratory
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
add a comment |
I want to train several times a file ConvNet.py in order to produce some statistics about its training like precision, confussion matrices, etc. So, I tried (in google colab) to do something like
for k in range(10:
%run ConvNet.py
The first training goes well, but when in begin the second, arise a problem. It says that "weights variable already defined, disallowed" (weights is the fist variable that I define in ConvNet.py) and the script stops.
I tried clearing variables with os kill, but there is still problems. How can I fix this?
python tensorflow google-colaboratory
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
I want to train several times a file ConvNet.py in order to produce some statistics about its training like precision, confussion matrices, etc. So, I tried (in google colab) to do something like
for k in range(10:
%run ConvNet.py
The first training goes well, but when in begin the second, arise a problem. It says that "weights variable already defined, disallowed" (weights is the fist variable that I define in ConvNet.py) and the script stops.
I tried clearing variables with os kill, but there is still problems. How can I fix this?
python tensorflow google-colaboratory
python tensorflow google-colaboratory
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
asked Dec 30 '18 at 23:26
Veridian DynamicsVeridian Dynamics
1336
1336
add a comment |
add a comment |
1 Answer
1
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oldest
votes
It's probably better if you do the iterated training in Python directly.
You haven't shared much about your setup, but you can do something like:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def build_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28,)))
model.add(tf.keras.layers.Dense(32, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="sigmoid"))
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
for i in range(10):
model = build_model()
model.fit(x_train, y_train)
model.save_weights(f"./weights-{i}.hdf5")
And when you want to do the analysis:
for i in range(10):
model = build_model()
model.load_weights(f"./weights-{i}.hdf5")
do_analysis(model)
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It's probably better if you do the iterated training in Python directly.
You haven't shared much about your setup, but you can do something like:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def build_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28,)))
model.add(tf.keras.layers.Dense(32, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="sigmoid"))
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
for i in range(10):
model = build_model()
model.fit(x_train, y_train)
model.save_weights(f"./weights-{i}.hdf5")
And when you want to do the analysis:
for i in range(10):
model = build_model()
model.load_weights(f"./weights-{i}.hdf5")
do_analysis(model)
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
add a comment |
It's probably better if you do the iterated training in Python directly.
You haven't shared much about your setup, but you can do something like:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def build_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28,)))
model.add(tf.keras.layers.Dense(32, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="sigmoid"))
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
for i in range(10):
model = build_model()
model.fit(x_train, y_train)
model.save_weights(f"./weights-{i}.hdf5")
And when you want to do the analysis:
for i in range(10):
model = build_model()
model.load_weights(f"./weights-{i}.hdf5")
do_analysis(model)
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
add a comment |
It's probably better if you do the iterated training in Python directly.
You haven't shared much about your setup, but you can do something like:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def build_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28,)))
model.add(tf.keras.layers.Dense(32, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="sigmoid"))
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
for i in range(10):
model = build_model()
model.fit(x_train, y_train)
model.save_weights(f"./weights-{i}.hdf5")
And when you want to do the analysis:
for i in range(10):
model = build_model()
model.load_weights(f"./weights-{i}.hdf5")
do_analysis(model)
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
It's probably better if you do the iterated training in Python directly.
You haven't shared much about your setup, but you can do something like:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def build_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28,)))
model.add(tf.keras.layers.Dense(32, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="sigmoid"))
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
for i in range(10):
model = build_model()
model.fit(x_train, y_train)
model.save_weights(f"./weights-{i}.hdf5")
And when you want to do the analysis:
for i in range(10):
model = build_model()
model.load_weights(f"./weights-{i}.hdf5")
do_analysis(model)
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
answered Dec 31 '18 at 17:38
Kasper FredenslundKasper Fredenslund
126113
126113
add a comment |
add a comment |
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