tensorflow - w3toppers.com

ImportError: libcudnn when running a TensorFlow program

Just download cuDNN 5.1 and follow the steps (Tested on Ubuntu 16.04, CUDA toolkit 8.0 ) $ tar xvzf cudnn-8.0-linux-x64-v5.1-ga.tgz $ sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* Now set Path variables $ vim ~/.bashrc export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64″ export CUDA_HOME=/usr/local/cuda and done For more details, you … Read more

How to display custom images in TensorBoard using Keras?

So, the following solution works well for me: import tensorflow as tf def make_image(tensor): “”” Convert an numpy representation image to Image protobuf. Copied from https://github.com/lanpa/tensorboard-pytorch/ “”” from PIL import Image height, width, channel = tensor.shape image = Image.fromarray(tensor) import io output = io.BytesIO() image.save(output, format=”PNG”) image_string = output.getvalue() output.close() return tf.Summary.Image(height=height, width=width, colorspace=channel, encoded_image_string=image_string) … Read more

How can I convert a trained Tensorflow model to Keras?

I think the callback in keras is also a solution. The ckpt file can be saved by TF with: saver = tf.train.Saver() saver.save(sess, checkpoint_name) and to load checkpoint in Keras, you need a callback class as follow: class RestoreCkptCallback(keras.callbacks.Callback): def __init__(self, pretrained_file): self.pretrained_file = pretrained_file self.sess = keras.backend.get_session() self.saver = tf.train.Saver() def on_train_begin(self, logs=None): if … Read more

How to properly use tf.metrics.accuracy?

TL;DR The accuracy function tf.metrics.accuracy calculates how often predictions matches labels based on two local variables it creates: total and count, that are used to compute the frequency with which logits matches labels. acc, acc_op = tf.metrics.accuracy(labels=tf.argmax(labels, 1), predictions=tf.argmax(logits,1)) print(sess.run([acc, acc_op])) print(sess.run([acc])) # Output #[0.0, 0.66666669] #[0.66666669] acc (accuracy): simply returns the metrics using total … Read more

Confusion about keras Model: call vs. call vs. predict methods

Adding to @Dmitry Kabanov, they are similar yet they aren’t exactly the same thing. If you care about performance, need to look in to critical differences between them. model.predict model(x) loops over the data in batches which means means that predict() calls can scale to very large arrays. happens in-memory and doesn’t scale not differentiable … Read more

Implementing contrastive loss and triplet loss in Tensorflow

Update (2018/03/19): I wrote a blog post detailing how to implement triplet loss in TensorFlow. You need to implement yourself the contrastive loss or the triplet loss, but once you know the pairs or triplets this is quite easy. Contrastive Loss Suppose you have as input the pairs of data and their label (positive or … Read more

Convert between NHWC and NCHW in TensorFlow

All you need to do is a permutation of the dimensions from NHWC to NCHW (or the contrary). The meaning of each letter might help understand: N: number of images in the batch H: height of the image W: width of the image C: number of channels of the image (ex: 3 for RGB, 1 … Read more

CUDA_ERROR_OUT_OF_MEMORY in tensorflow

In case it’s still relevant for someone, I encountered this issue when trying to run Keras/Tensorflow for the second time, after a first run was aborted. It seems the GPU memory is still allocated, and therefore cannot be allocated again. It was solved by manually ending all python processes that use the GPU, or alternatively, … Read more

In Keras, what exactly am I configuring when I create a stateful `LSTM` layer with N `units`?

You can check this question for further information, although it is based on Keras-1.x API. Basically, the unit means the dimension of the inner cells in LSTM. Because in LSTM, the dimension of inner cell (C_t and C_{t-1} in the graph), output mask (o_t in the graph) and hidden/output state (h_t in the graph) should … Read more

Keras split train test set when using ImageDataGenerator

Keras has now added Train / validation split from a single directory using ImageDataGenerator: train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, validation_split=0.2) # set validation split train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode=”binary”, subset=”training”) # set as training data validation_generator = train_datagen.flow_from_directory( train_data_dir, # same directory as training data target_size=(img_height, img_width), batch_size=batch_size, class_mode=”binary”, subset=”validation”) # … Read more