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
Related
I tried to use numpy inside cnn_model.evaluate(), but it gave AttributeError: 'Tensor' object has no attribute 'numpy'. I used numpy to calculate accuracy and mean squared error using tf.keras.metrics.Accuracy() and tf.keras.metrics.MeanSquaredError() inside cnn_model.evaluate()
I googled it, and in tensorflow documentation, it said
"Calling methods of Estimator will work while eager execution is enabled. However, the model_fn and input_fn is not executed eagerly, Estimator will switch to graph mode before calling all user-provided functions (incl. hooks), so their code has to be compatible with graph mode execution."
So, I was wondering how I can update the current tf 1.x code to tf 2.1.0 code, while also using above information.
My current code is:
eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={"x": np.array(train_inputs, dtype=np.float32)},
y=np.array(train_labels, dtype=np.float32),
#y=np.array(train_labels),
batch_size=1,
num_epochs=1,
shuffle=False)
eval_results = CNN.evaluate(input_fn=eval_input_fn)
What I have tried so far is add tf.compat.v1.enable_eager_execution() to the 1) beginning of the code after all the imports, 2) next line right after importing tf, 3) line right before declaring eval_input_fn, 4) line right before calling eval_results, 5) inside CNN model definition. It all failed to turn on the eager mode.
One other option that I found was remove #tf.function decorator, but I have no idea what that means and how to pass input_fn if #tf.function is removed.
My Goal
I want to train a custom object detection model in Tensorflow(python) and use it using Tensorflow js after digging lot of example I found this which is widely popular
What I have done
I have written ( taken help form online examples ) the Tensorflow JS part to load a model from local and get the get the predictions. I used with COCO pretrained model It is working fine (so not adding the code here).
What is my problem
I am very new to python and Tensorflow.
The example for training qqwweee/keras-yolo3 the model is in python and it is Lamda from Keras
from keras.layers import Input, Lambda for this places
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
And
model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
And
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
So What I understood so far, Lambda is mainly used for calculating the loss function, and this is causing main problem in TFJS because Lambda layer is not implemented till now I want to use some alternative instead of lambda layer.
This is the error I am getting while using the trained model in TFJS
Error loading layer ValueError: Unknown layer: Lambda. This may be due to one of the following reasons:
1. The layer is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom layer is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().
Similar question is also asked here "Unknown layer: Lambda" in tensorflowjs on browser, it talks about writing a custom layer , the example is not enough to do that, ultimately leads to dead end.
What I want
Is there any way to use any other loss function insteed of lambda ? how?
Is there any example for writing custom layer for lambda
Where are my understanding are wrong?
p.s: I spent hell lot of time to find the solution, any help will be appreciated, Thanks in advance
After adding the empty lambda layer given by #edkeveked (Thanks!), the error Error loading layer ValueError: Unknown layer: Lambda is gone but ran into something else.
Check the model summary here
Now, In the model warmup itslef thorowing this error
code for warmup
let zero = tfNode.zeros([1, 416, 416, 3]);
const result = await this.model.predict(zero)
result.map(async (t) => await t.data());
result.map(async (t) => t.dispose());
code for image prediction
batched = tfNode.tidy(() => {
if (!(img instanceof tfNode.Tensor)) {
img = tfNode.browser.fromPixels(img);
}
return img.expandDims(0);
});
result = await this.model.predict(batched);
Error I am getting
"Error: Error when checking model : the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see 4 Tensor(s), but instead got 1 Tensors(s).
at new ValueError (XXX\node_modules\#tensorflow\tfjs-layers\dist\errors.js:68:28)
at checkInputData (XXX\node_modules\#tensorflow\tfjs-layers\dist\engine\training.js:316:19)
at LayersModel.predict (XXX\node_modules\#tensorflow\tfjs-layers\dist\engine\training.js:981:9)
at ObjectDetection.warmUp (XXX\tensorflow_predownloaded_model.js:47:45)
at XXX\tensorflow_predownloaded_model.js:38:18"
Since the Lambda layer is not yet supported, it need to be provided for the conversion to work.
Moreover, the loaded layer is not used for training, so the lambda layer can be empty. (code not tried)
class Lambda extends tf.layers.Layer {
constructor() {
super({})
}
static get className() {
return 'Lambda';
}
}
tf.serialization.SerializationMap.register(Lambda);
;
I'm quite familiar in TensorFlow 1.x and I'm considering to switch to TensorFlow 2 for an upcoming project. I'm having some trouble understanding how to write scalars to TensorBoard logs with eager execution, using a custom training loop.
Problem description
In tf1 you would create some summary ops (one op for each thing you would want to store), which you would then merge into a single op, run that merged op inside a session and then write this to a file using a FileWriter object. Assuming sess is our tf.Session(), an example of how this worked can be seen below:
# While defining our computation graph, define summary ops:
# ... some ops ...
tf.summary.scalar('scalar_1', scalar_1)
# ... some more ops ...
tf.summary.scalar('scalar_2', scalar_2)
# ... etc.
# Merge all these summaries into a single op:
merged = tf.summary.merge_all()
# Define a FileWriter (i.e. an object that writes summaries to files):
writer = tf.summary.FileWriter(log_dir, sess.graph)
# Inside the training loop run the op and write the results to a file:
for i in range(num_iters):
summary, ... = sess.run([merged, ...], ...)
writer.add_summary(summary, i)
The problem is that sessions don't exist anymore in tf2 and I would prefer not disabling eager execution to make this work. The official documentation is written for tf1 and all references I can find suggest using the Tensorboard keras callback. However, as far as I know, this only works if you train the model through model.fit(...) and not through a custom training loop.
What I've tried
The tf1 version of tf.summary functions, outside of a session. Obviously any combination of these functions fails, as FileWriters, merge_ops, etc. don't even exist in tf2.
This medium post states that there has been a "cleanup" in some tensorflow APIs including tf.summary(). They suggest using from tensorflow.python.ops.summary_ops_v2, which doesn't seem to work. This implies using a record_summaries_every_n_global_steps; more on this later.
A series of other posts 1, 2, 3, suggest using the tf.contrib.summary and tf.contrib.FileWriter. However, tf.contrib has been removed from the core TensorFlow repository and build process.
A TensorFlow v2 showcase from the official repo, which again uses the tf.contrib summaries along with the record_summaries_every_n_global_steps mentioned previously. I couldn't make this to work either (even without using the contrib library).
tl;dr
My questions are:
Is there a way to properly use tf.summary in TensroFlow 2?
If not, is there another way to write TensorBoard logs in TensorFlow 2, when using a custom training loop (not model.fit())?
Yes, there is a simpler and more elegant way to use summaries in TensorFlow v2.
First, create a file writer that stores the logs (e.g. in a directory named log_dir):
writer = tf.summary.create_file_writer(log_dir)
Anywhere you want to write something to the log file (e.g. a scalar) use your good old tf.summary.scalar inside a context created by the writer. Suppose you want to store the value of scalar_1 for step i:
with writer.as_default():
tf.summary.scalar('scalar_1', scalar_1, step=i)
You can open as many of these contexts as you like inside or outside of your training loop.
Example:
# create the file writer object
writer = tf.summary.create_file_writer(log_dir)
for i, (x, y) in enumerate(train_set):
with tf.GradientTape() as tape:
y_ = model(x)
loss = loss_func(y, y_)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# write the loss value
with writer.as_default():
tf.summary.scalar('training loss', loss, step=i+1)
I'm trying to add some TensorBoard logging to a model which uses the new tf.estimator API.
I have a hook set up like so:
summary_hook = tf.train.SummarySaverHook(
save_secs=2,
output_dir=MODEL_DIR,
summary_op=tf.summary.merge_all())
# ...
classifier.train(
input_fn,
steps=1000,
hooks=[summary_hook])
In my model_fn, I am also creating a summary -
def model_fn(features, labels, mode):
# ... model stuff, calculate the value of loss
tf.summary.scalar("loss", loss)
# ...
However, when I run this code, I get the following error from the summary_hook:
Exactly one of scaffold or summary_op must be provided. This is probably because tf.summary.merge_all() is not finding any summaries and is returning None, despite the tf.summary.scalar I declared in the model_fn.
Any ideas why this wouldn't be working?
Use tf.train.Scaffold() and pass tf.merge_all as following
summary_hook = tf.train.SummarySaverHook(
save_secs=2,
output_dir=MODEL_DIR,
scaffold=tf.train.Scaffold(summary_op=tf.summary.merge_all()))
Just for whoever have this question in the future, the selected solution doesn't work for me (see my comments in the selected solution).
Actually, with TF 1.2 Estimator API, one doesn't need to have summary_hook. I just have tf.summary.scalar("loss", loss) in the model_fn, and run the code without summary_hook. The loss is recorded and shown in the tensorboard. I'm not sure if TF API was changed after this and similar questions.
with Tensorflow ver-r1.3
Add your summary ops in your estimator model_fn
example :
tf.summary.histogram(tensorOp.name, tensorOp)
If you feel writing summaries may consume time and space, you can control the writing frequency of summaries, in your Estimator run_config
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(model_dir=FLAGS.model_dir)
run_config = run_config.replace(save_summary_steps=150)
Note: this will affect the overall summary writer frequency for TensorBoard logging, of your estimator (tf.estimator.Estimator)
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