2.0 Update (10/8/19) Setting TF_CPP_MIN_LOG_LEVEL should still work (see below in v0.12+ update), but there is currently an issue open (see issue #31870). If setting TF_CPP_MIN_LOG_LEVEL does not work for you (again, see below), try doing the following to set the log level: import tensorflow as tf tf.get_logger().setLevel(‘INFO’) In addition, please see the documentation on … Read more
When you saved your model using: old_model.save(‘my_model.h5’) it will save following: The architecture of the model, allowing to create the model. The weights of the model. The training configuration of the model (loss, optimizer). The state of the optimizer, allowing training to resume from where you left before. So then, when you load the model: … Read more
Since Python 3.7 you can use a built-in breakpoint function. If this is not available, you can use import pdb pdb.set_trace() instead. If you want to execute the next line you can try n (next) instead of s (step).
In Deep Neural Networks the depth refers to how deep the network is but in this context, the depth is used for visual recognition and it translates to the 3rd dimension of an image. In this case you have an image, and the size of this input is 32x32x3 which is (width, height, depth). The … Read more
Both frameworks operate on tensors and view any model as a directed acyclic graph (DAG), but they differ drastically on how you can define them. TensorFlow follows ‘data as code and code is data’ idiom. In TensorFlow you define graph statically before a model can run. All communication with outer world is performed via tf.Session … Read more
Try the following callback: class TimeHistory(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.times = [] def on_epoch_begin(self, batch, logs={}): self.epoch_time_start = time.time() def on_epoch_end(self, batch, logs={}): self.times.append(time.time() – self.epoch_time_start) Then: time_callback = TimeHistory() model.fit(…, callbacks=[…, time_callback],…) times = time_callback.times In this case times should store the epoch computation times.
To understand how backpropagation is even possible with functions like ReLU you need to understand what is the most important property of derivative that makes backpropagation algorithm works so well. This property is that : f(x) ~ f(x0) + f'(x0)(x – x0) If you treat x0 as actual value of your parameter at the moment … Read more
We used deep convolutional networks on spectrograms for a spoken language identification task. We had around 95% accuracy on a dataset provided in this TopCoder contest. The details are here. Plain convolutional networks do not capture the temporal characteristics, so for example in this work the output of the convolutional network was fed to a … Read more
From the recent Deep Learning book by Goodfellow et al., chapter 8: Minibatch sizes are generally driven by the following factors: Larger batches provide a more accurate estimate of the gradient, but with less than linear returns. Multicore architectures are usually underutilized by extremely small batches. This motivates using some absolute minimum batch size, below … Read more