The whole process is simplied using the Dataset API. Here are both the parts: (1): Convert numpy array to tfrecords and (2): read the tfrecords to generate batches. 1. Creation of tfrecords from a numpy array: Example arrays: inputs = np.random.normal(size=(5, 32, 32, 3)) labels = np.random.randint(0,2,size=(5,)) def npy_to_tfrecords(inputs, labels, filename): with tf.io.TFRecordWriter(filename) as writer: … Read more
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
If you only need TensorFlow because of Keras and your are on Python 2.7.x, you can avoid installing Tensorflow(Google) and replace it by CNTK(Microsoft). According to Jeong-Yoon Lee CNTK is a lot (about 2 to 4 times) faster than TensorFlow for LSTM (Bidirectional LSTM on IMDb Data and Text Generation via LSTM), while speeds for … Read more
Variable is basically a wrapper on Tensor that maintains state across multiple calls to run, and I think makes some things easier with saving and restoring graphs. A Variable needs to be initialized before you can run it. You provide an initial value when you define the Variable, but you have to call its initializer … Read more
Recent GPU versions of tensorflow require compute capability 3.5 or higher (and use cuDNN to access the GPU. cuDNN also requires a GPU of cc3.0 or higher: cuDNN is supported on Windows, Linux and MacOS systems with Pascal, Kepler, Maxwell, Tegra K1 or Tegra X1 GPUs. Kepler = cc3.x Maxwell = cc5.x Pascal = cc6.x … Read more
This is a common use case, and most implementations use TensorFlow’s queues to decouple the preprocessing code from the training code. There is a tutorial on how to use queues, but the main steps are as follows: Define a queue, q, that will buffer the preprocessed data. TensorFlow supports the simple tf.FIFOQueue that produces elements … Read more
from keras.models import Model from keras.layers import * #inp is a “tensor”, that can be passed when calling other layers to produce an output inp = Input((10,)) #supposing you have ten numeric values as input #here, SomeLayer() is defining a layer, #and calling it with (inp) produces the output tensor x x = SomeLayer(blablabla)(inp) x … Read more
If you want to get weights and biases of all layers, you can simply use: for layer in model.layers: print(layer.get_config(), layer.get_weights()) This will print all information that’s relevant. If you want the weights directly returned as numpy arrays, you can use: first_layer_weights = model.layers[0].get_weights()[0] first_layer_biases = model.layers[0].get_weights()[1] second_layer_weights = model.layers[1].get_weights()[0] second_layer_biases = model.layers[1].get_weights()[1] etc.
You will need to update your LD_LIBRARY_PATH, so that it points to the /usr/local/cuda-9.0/lib64. Add the following line to your .bashrc file (or any other terminal you use) export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-9.0/lib64/
Save the initial weights right after compiling the model but before training it: model.save_weights(‘model.h5’) and then after training, “reset” the model by reloading the initial weights: model.load_weights(‘model.h5’) This gives you an apples to apples model to compare different data sets and should be quicker than recompiling the entire model.