I think I'm losing my mind at this point.
I'm using Lasagne for a small convolutional neural network. It trains perfectly, I can compute the error on training and validation set as well, but I cannot save the trained model on the disk. Better, I can save it and load it, but I cannot use it to predict for new data.
This is what I do after training
model = {'network': network, 'params': get_all_params(network), 'params_values': get_all_param_values(network)}
pickle.dump(model, open('models/model_1.pkl', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
And this is what I do to load the model
with open('models/model.pkl', 'rb') as pickle_file:
model = pickle.load(pickle_file)
network = model['network']
values = model['params_values']
set_all_param_values(network, values)
T_input = T.tensor4('input', dtype='float32')
T_target = T.ivector('target')
predictions = get_output(network, deterministic=True)
loss = (cross_entropy(predictions, T_target)).mean()
acc = T.mean(T.eq(T.argmax(predictions, axis=1), T_target), dtype=config.floatX)
test_fn = function([T_input, T_target], [loss, acc])
I cannot even pass the real numpy input, that I get this error
theano.compile.function_module.UnusedInputError: theano.function was asked to create a
function computing outputs given certain inputs, but the provided input variable at index 0
is not part of the computational graph needed to compute the outputs: input.
To make this error into a warning, you can pass the parameter
on_unused_input='warn' to theano.function. To disable it completely, use
on_unused_input='ignore'.
I tried to set the parameter on_unused_input='warn' then, and this is the result
theano.gof.fg.MissingInputError: An input of the graph, used to compute (..)
was not provided and not given a value.Use the Theano flag
exception_verbosity='high',for more information on this error.
The problem is that your T_input is not tied to the input layer and hence theano can't compile it
T_input = lasagne.layers.get_all_layers(network)[0].input_var
Related
thanks for your atention, I'm developing an automatic speaker recognition system using SincNet.
Ravanelli, M., & Bengio, Y. (2018, December). Speaker recognition from raw waveform with sincnet. In 2018 IEEE Spoken Language Technology Workshop (SLT) (pp. 1021-1028). IEEE.
Since the network is coded in Pytorch I searched and found a Keras implementation here https://github.com/grausof/keras-sincnet. I adapted the train.py code to train a Sincnet with my own data in Tensorflow 2.0, and worked fine, I saved only the weights of my trained network, my training data has shape 128,3200,1 for inputs and 128 for labels per batch
#Creates a Sincnet model with input_size=3200 (wlen), num_classes=40, fs=16000
redsinc = create_model(wlen,num_classes,fs)
#Saves only weights and stopearly callback
checkpointer = ModelCheckpoint(filepath='checkpoints/SincNetBiomex3.hdf5',verbose=1,
save_best_only=True, monitor='val_accuracy',save_weights_only=True)
stopearly = EarlyStopping(monitor='val_accuracy',patience=3,verbose=1)
callbacks = [checkpointer,stopearly]
# optimizer = RMSprop(lr=learnrate, rho=0.9, epsilon=1e-8)
optimizer = Adam(learning_rate=learnrate)
# Creates generator of training batches
train_generator = batchGenerator(batch_size,train_inputs,train_labels,wlen)
validinputs, validlabels = create_batches_rnd(validation_labels.shape[0],
validation_inputs,validation_labels,wlen)
#Compiling model and train with function fit_generator
redsinc.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
history = redsinc.fit_generator(train_generator, steps_per_epoch=N_batches, epochs = epochs,
verbose = 1, callbacks=callbacks, validation_data=(validinputs,validlabels))
The problem came when I tried to evaluate the network, I didn't use the code found in test.py, I only loaded the weights I previously saved and use the function evaluate, my test data had the shape 1200,3200,1 for the inputs and 1200 for labels.
# Create a Sincnet model and load previously saved weights
redsinc = create_model(wlen,num_clases,fs)
redsinc.load_weights('checkpoints/SincNetBiomex3.hdf5')
test_loss, test_accuracy = redsinc.evaluate(x=eval_in,y=eval_lab)
RuntimeError: You must compile your model before training/testing. Use `model.compile(optimizer,
loss)`.
Then I added the same compile code I used for training:
optimizer = Adam(learning_rate=0.001)
redsinc.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
Then rerun the test code and got this:
WARNING:tensorflow:From C:\Users\atenc\Anaconda3\envs\py3.7-tf2.0gpu\lib\site-
packages\tensorflow_core\python\ops\resource_variable_ops.py:1781: calling
BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is
deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
ValueError: A tf.Variable created inside your tf.function has been garbage-collected. Your code needs to keep Python references to variables created inside `tf.function`s.
A common way to raise this error is to create and return a variable only referenced inside your function:
#tf.function
def f():
v = tf.Variable(1.0)
return v
v = f() # Crashes with this error message!
The reason this crashes is that #tf.function annotated function returns a **`tf.Tensor`** with the **value** of the variable when the function is called rather than the variable instance itself. As such there is no code holding a reference to the `v` created inside the function and Python garbage collects it.
The simplest way to fix this issue is to create variables outside the function and capture them:
v = tf.Variable(1.0)
#tf.function
def f():
return v
f() # <tf.Tensor: ... numpy=1.>
v.assign_add(1.)
f() # <tf.Tensor: ... numpy=2.>
I don't understand the error since I've evaluated other networks with the same function and never got any problems. Then I decided to use predict function to match predicted labels with correct labels and obtain all metrics with my own code but I got another error.
# Create a Sincnet model and load previously saved weights
redsinc = create_model(wlen,num_clases,fs)
redsinc.load_weights('checkpoints/SincNetBiomex3.hdf5')
print('Model loaded')
#Predict labels with test data
predict_labels = redsinc.predict(eval_in)
Error while reading resource variable _AnonymousVar212 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/_AnonymousVar212/class tensorflow::Var does not exist.
[[node sinc_conv1d/concat_104/ReadVariableOp (defined at \Users\atenc\Anaconda3\envs\py3.7-tf2.0gpu\lib\site-packages\tensorflow_core\python\framework\ops.py:1751) ]] [Op:__inference_keras_scratch_graph_13649]
Function call stack:
keras_scratch_graph
I hope someone can tell me what these errors mean and how to solve them, I've searched for solutions to them but most of the solutions I've found don't seem related to my problem so I can't apply those solutions. I'm guessing the errors are caused by the Sincnet layer code, because it is a custom coded layer. The code for Sincnet layer can be found in the github repository in the file sincnet.py.
I appreciate all help I can get, again thank you for your atention.
You should downgrade your tf and keras version, it works to me when I faced the same problem.
Try this keras==2.1.6; tensorflow-gpu==1.13.1
I am using Chainer's pertained model vgg (here named net). Every time I run the following code, I get a different result:
img = Image.open("/Users/macintosh/Desktop/Code/Ger.jpg")
img = Variable(vgg.prepare(img))
img = img.reshape((1,) + img.shape)
print(net(img,layers=['prob'])['prob'])
I have checked vgg.prepare() several times but its output is the same, and there is no random initialization here (net is a pre-trained vgg network). So why is this happening?
As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
I know this question is asked more than one time, but I couldn't understand codes or the logic behind.
In my data set, first I created a layer, sigmoid layer, then I connected this layer to the output layer and I've used softmax function in the output layer.
fl = tf.layers.dense(x, 10,activation=tf.sigmoid)
output = tf.layers.dense(fl, 2,activation=tf.nn.softmax)
I've created loss and accuracy, initialized variables, set optimizer and train variables, then I start running on my data:
loss = tf.losses.softmax_cross_entropy(onehot_labels=y,logits=output)
accuracy = tf.metrics.accuracy(tf.argmax(y_train,1),tf.argmax(output,1))
# inits
init_local = tf.local_variables_initializer()
init_global = tf.global_variables_initializer()
sess.run(init_global)
sess.run(init_local)
optimizer = tf.train.GradientDescentOptimizer(rate)
train = optimizer.minimize(loss)
for i in range(1000):
_, lv = sess.run((train, loss))
if i%5 == 0:
print("L: " + str(lv))
print("Accuracy: "+str(sess.run(accuracy)))
I can see that my loss value decreases every time I run on the training set. And my accuracy is ~0.93.
The problem is, from now on, I don't know how to test this model with real data.
Also, how can I draw a histogram of my real data? I have correct labels for my real data as well.
I will assume that you use Dataset to feed your training data and you want to run on test data immediately after training (since you don't have checkpoints in your code).
When using Dataset, you would create an iterator and call get_next() on it. Then, you would use the return values of get_next() as inputs to your model.
To run your model on the test data, you can use two high-level approaches:
If you test data has the same format as you train data, create a dataset that reads your test data. Then, create another copy (sometimes called a "tower") of your model (operations will be new but variables will be shared) that uses the test Dataset. Then, use sess.run() similarly to how you use it for training - you might not need to compute loss or train, but only accuracy.
If you test data has different format, you can feed it directly, by using feed_dict argument to sess.run(). You would feed your test data as values for tensors returned from get_next(). Usually, one feeds placeholders, but TensorFlow allows you to feed any tensor.
As for histograms, Tensorboard has a nice way of visualizing them: https://www.tensorflow.org/programmers_guide/tensorboard_histograms.
I have a meta and checkpoint files from which I load the weights of a pre-trained model. This works fine.
To test this model with a new image I need to do some pre-processing for the image (converting from grayscale to RGB, etc) which is basically done using opencv library. Doing this I do get my desired output.
But now what I want to do is add this pre-processing code to tensorflow itself so that when I save this model and re-use it I can only pass the image path as an argument and I don't need to do any pre-processing before passing it to tensorflow. I want tensorflow to handle all this.
I have tried the following already
The following I have used to implement the preprocessing the images within the tensorflow itself and save the new meta and checkpoint files
graph = tf.Graph()
with graph.as_default():
def dataprocess(x):
#convert from gray to rgb,etc
return y
path = ["images/test.jpg"]
filenames = tf.constant(path)
dataset = tf.contrib.data.Dataset.from_tensor_slices((filenames))
dataset = dataset.map(
lambda path : tf.py_func(
dataprocess[path], [tf.float32]))
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
next_element = tf.reshape(next_element,[-1,3,224,224]) #reshape as tensorflow shows unknown
The below code I use to restore my previous model
with tf.Session(graph=graph) as sess:
sess.run(iterator.initializer)
element1 = sess.run(next_element)
saver = tf.train.import_meta_graph('./.meta')
saver.restore(sess,'./')
saver1 = tf.train.Saver()
input= graph.get_tensor_by_name('input_1:0')
output= graph.get_tensor_by_name('predictions/Sigmoid:0')
print(sess.run(output,{input:element1}))
saver1.save(sess,'/tmp/test1/')
This all works fine
Next, I use the newly created meta and checkpoint files to test for an image(path)
path = ["images/test.jpg"]
with tf.Session() as sess:
saver = tf.train.import_meta_graph('./.meta')
saver.restore(sess,'./')
graph = tf.get_default_graph()
input = graph.get_tensor_by_name('Const:0')
output= graph.get_tensor_by_name('predictions/Sigmoid:0')
print(sess.run(output,{input:path}))
Using I get the following error
InvalidArgumentError: You must feed a value for placeholder tensor 'input_1' with dtype float and shape [?,3,224,224]
[[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=[?,3,224,224], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
input_1 corresponds to the above input of the graph
So I am assuming I am not passing the path to the correct place
I am new to tensorflow and there is pretty less documentation regarding this
Thank you
But now what I want to do is add this pre-processing code to
tensorflow itself so that when I save this model and re-use it I can
only pass the image path as an argument and I don't need to do any
pre-processing before passing it to tensorflow. I want tensorflow to
handle all this.
Unfortunately this is not possible if you use the tf.py_func operation. When you save the graph the python code inside the tf.py_func will not be saved as it is not part of the graph. (See py_func limitations here.) The only way to make the pre-processing part of the graph would be to rewrite it with tensorflow code without using the tf.py_func.
I am starting to use caffe for deep learning. I have the .caffemodel file with my trained weights and a particular neural network. I am using python interface.
I've seen that I can load my network and my weights by doing this:
solver=caffe.get_solver('prototxtfile.prototxt')
solver.net.copy_from('weights.caffemodel')
But I do not want to fine-tuned my application. I just want to use those weights. I want to execute the network and for each image from the Imagenet data set I want to obtain the result of the classification (not the accuracy of an entire batch). How can I do that?
Thank you very much.
Try to understand the attached lines of python code and adjust them to your needs. It's not my code but I wrote a similar piece to test my models.
The source is:
https://www.cc.gatech.edu/~zk15/deep_learning/classify_test.py
If you don't want to fine-tune a pre-trained model, it's obvious that you don't need a solver. The solver is what optimizes the model. If you want to predict the class probability for an image, you actually just have to do a forward pass. Keep in mind that your deploy.prototxt must have a proper last layer which uses either a softmax or sigmoid function (depending on the architecture). You can't use the loss function from the train_val.prototxt for this.
import numpy as np
import matplotlib.pyplot as plt
# Make sure that caffe is on the python path:
caffe_root = '../' # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
# Set the right path to your model definition file, pretrained model weights,
# and the image you would like to classify.
MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'
PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
IMAGE_FILE = 'images/cat.jpg'
caffe.set_mode_cpu()
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),
channel_swap=(2,1,0),
raw_scale=255,
image_dims=(256, 256))
input_image = caffe.io.load_image(IMAGE_FILE)
plt.imshow(input_image)
prediction = net.predict([input_image]) # predict takes any number of images, and formats them for the Caffe net automatically
print 'prediction shape:', prediction[0].shape
plt.plot(prediction[0])
print 'predicted class:', prediction[0].argmax()
plt.show()
This is the code I use when I need to forward an image through my network:
import caffe
caffe.set_mode_cpu() #If you are using CPU
#caffe.set_mode_gpu() #or if you are using GPU
model_def = "path/to/deploy.prototxt" #architecture
model_weights = "path/to/weights.caffemodel" #weights
net = caffe.Net(model_def, # defines the structure of the model
model_weights,
caffe.TEST) # use test mode (e.g., don't perform dropout)
#Let's forward a single image (let's say inputImg)
#'data' is the name of my input blob
net.blobs["data"].data[0] = inputImg
out = net.forward()
# to get the final softmax probability
# in my case, 'prob' is the name of our last blob
# a softmax layer that will output the score/probability for our problem
outputScore = net.blobs["prob"].data[0] #[0] here because we forwarded a single image
In this example, the inputImg dimensions must match the dimensions of the images used during training, as well as all preprocessing done.