AlexNet 17 flowers - Evaluating not possible / Easier evaluating method? about tflearn HOT 5 CLOSED

Using model.predict() works for me to predict some images. If it's helpful for others, this is the code I am using:

from PIL import Image
import numpy as np

img = Image.open("17flowers/jpg/0/image_0006.jpg")
img = img.resize((224, 224), Image.ANTIALIAS)
img = np.asarray(img, dtype="float32")

img2 = Image.open("17flowers/jpg/1/image_0096.jpg")
img2 = img2.resize((224, 224), Image.ANTIALIAS)
img2 = np.asarray(img2, dtype="float32")

imgs = np.asarray([img, img2, img3])

So I create an array with two images for prediction. Then I am building my network and train it.

network = ...

model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                max_checkpoints=1, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=100, validation_set=0.1, shuffle=True,
      show_metric=True, batch_size=32, snapshot_step=400,
      snapshot_epoch=False, run_id='alexnet_oxflowers17')

And make a prediction of the two images I loaded in the beginning:

results = model.predict(imgs)
print(np.argmax(results[0]))
print(np.amax(results[0]))
print(np.argmax(results[1]))
print(np.amax(results[1]))

At the moment, the model predicts 1 of 2 or 1 of 3 images right, depending on the images loaded. I will have to train longer to get better results, my network was at an accuracy of ~70% when I got those prediction results.

Thanks for your help @aymericdamien !

from tflearn.

tensorflow.python.framework.errors.ResourceExhaustedError: OOM when allocating tensor with shape[12544,4096]
How did you fix this problem? I encounter this while training AlexNet but I don't know how to fix it.

from tflearn.

I tried to train alexnet and I got ResourceExhaustedError also. Can anyone help?
GPU: GTX970M with 3GB memory

from tflearn.

Actually, there is not evaluate method available now, the one available for DNN model (.evaluate()) has issue, but it should be fix soon.
The only way now would be to use model.predict(...) to predict all your data per batch, and loop over all your test set and calculate accuracy by yourself.

from tflearn.

Nice! Note that this dataset is quite small, so it may not be very accurate.
I will add a .evaluate() method for DNN model class, so it will be easy to evaluate a model on any test set.

from tflearn.