I trained my CNN (VGG) through google colab and generated .h5 file. Now problem is, I can predict my output successfully through google colab but when i download that .h5 trained... moreI trained my CNN (VGG) through google colab and generated .h5 file. Now problem is, I can predict my output successfully through google colab but when i download that .h5 trained model file and try to predict output on my laptop, I am getting error when loading the model.
Here is the code:
import tensorflow as tf
from tensorflow import keras
import h5py
I have a data matrix in "one-hot encoding" (all ones and zeros) with 260,000 rows and 35 columns. I am using Keras to train a simple neural network to predict a continuous... moreI have a data matrix in "one-hot encoding" (all ones and zeros) with 260,000 rows and 35 columns. I am using Keras to train a simple neural network to predict a continuous variable. The code to make the network is the following:
model = Sequential()
model.add(Dense(1024, input_shape=(n_train,)))
model.add(Activation('relu'))
model.add(Dropout(0.1))
sgd = SGD(lr=0.01, nesterov=True);
#rms = RMSprop()
#model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=)
model.compile(loss='mean_absolute_error', optimizer=sgd)
model.fit(X_train, Y_train, batch_size=32, nb_epoch=3, verbose=1, validation_data=(X_test,Y_test), callbacks= )
However, during the training process, I see the loss decrease nicely, but during the middle of the second epoch, it goes to nan:
Train on 260000 samples, validate on 64905 samples
Epoch... less
While trying to validate the installation of tensorflow-gpu, I get an ImportError when trying to execute "import tensorflow as tf". I am using a Quadro K620 on Windows 7.... moreWhile trying to validate the installation of tensorflow-gpu, I get an ImportError when trying to execute "import tensorflow as tf". I am using a Quadro K620 on Windows 7. Tensorflow was installed using pip.
The following is the stack trace:
Microsoft Windows
Copyright (c) 2009 Microsoft Corporation. All rights reserved.
C:\Users\aagarwal>python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
Traceback (most recent call last):
File "C:\Users\aagarwal\AppData\Local\Programs\Python\Python35\lib\site-packag
es\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_hel
per
return importlib.import_module(mname)
File "C:\Users\aagarwal\AppData\Local\Programs\Python\Python35\lib\importlib\_
_init__.py", line 126, in import_module
return _bootstrap._gcd_import(name, package, level)
File "<frozen importlib._bootstrap>", line 986, in _gcd_import
File... less
I'm saving my session state like so:
self._saver = tf.saver()
self._saver.save(self._session, '/network',... moreI'm saving my session state like so:
self._saver = tf.saver()
self._saver.save(self._session, '/network', global_step=self._time)
When I later restore I want to get the value of the global_step for the checkpoint I restore from. This is in order to set some hyper parameters from it.
The hacky way to do this would be to run through and parse the file names in the checkpoint directory. But surly there has to be a better, built in way to do this?
I am working on training a VGG16-like model in Keras, on a 3 classes subset from Places205, and encountered the following error:
ValueError: Error when checking target: expected... moreI am working on training a VGG16-like model in Keras, on a 3 classes subset from Places205, and encountered the following error:
ValueError: Error when checking target: expected dense_3 to have shape (3,) but got array with shape (1,)
I read multiple similar issues but none helped me so far. The error is on the last layer, where I've put 3 because this is the number of classes I'm trying right now.
The code is the following:
import keras from keras.datasets
import cifar10 from keras.preprocessing.image
import ImageDataGenerator from keras.models
import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K import os
I am trying an Op that is not behaving as expected.
graph = tf.Graph()
with graph.as_default():
train_dataset = tf.placeholder(tf.int32,... moreI am trying an Op that is not behaving as expected.
graph = tf.Graph()
with graph.as_default():
train_dataset = tf.placeholder(tf.int32, shape=)
embeddings = tf.Variable(
tf.random_uniform(, -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_dataset)
embed = tf.reduce_sum(embed, reduction_indices=0)
So I need to know the dimensions of the Tensor embed. I know that it can be done at the run time but it's too much work for such a simple operation. What's the easier way to do it?
Is there a way to plot both the training losses and validation losses on the same graph?
It's easy to have two separate scalar summaries for each of them individually, but this... moreIs there a way to plot both the training losses and validation losses on the same graph?
It's easy to have two separate scalar summaries for each of them individually, but this puts them on separate graphs. If both are displayed in the same graph it's much easier to see the gap between them and whether or not they have begin to diverge due to overfitting.
Is there a built in way to do this? If not, a work around way? Thank you much!
I have been using the introductory example of matrix multiplication in TensorFlow.
matrix1 = tf.constant()
matrix2 = tf.constant(,)
product = tf.matmul(matrix1,... moreI have been using the introductory example of matrix multiplication in TensorFlow.
matrix1 = tf.constant()
matrix2 = tf.constant(,)
product = tf.matmul(matrix1, matrix2)
When I print the product, it is displaying it as a Tensor object:
<tensorflow.python.framework.ops.Tensor object at 0x10470fcd0>
But how do I know the value of product?The following doesn't help:
print product Tensor("MatMul:0", shape=TensorShape(), dtype=float32)
I know that graphs run on Sessions, but isn't there any way I can check the output of a Tensor object without running the graph in a session? less
When I run sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) I get InternalError: Blas SGEMM launch failed. Here is the full error and stack trace:... moreWhen I run sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) I get InternalError: Blas SGEMM launch failed. Here is the full error and stack trace:
InternalErrorTraceback (most recent call last)
<ipython-input-9-a3261a02bdce> in <module>()
1 batch_xs, batch_ys = mnist.train.next_batch(100)
----> 2 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
338 try:
339 result = self._run(None, fetches, feed_dict, options_ptr,
--> 340 run_metadata_ptr)
341 if run_metadata:
342 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
562 try:
563 results = self._do_run(handle, target_list, unique_fetches,
--> 564 ... less
tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None)
I cannot understand the duty of this function. Is it like a lookup table? Which means to return the... moretf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None)
I cannot understand the duty of this function. Is it like a lookup table? Which means to return the parameters corresponding to each id (in ids)?For instance, in the skip-gram model if we use tf.nn.embedding_lookup(embeddings, train_inputs), then for each train_input it finds the correspond embedding?
