File "train.caltech.py", line 67, in detector = Detector(weight_path, n_labels) File "/home/spark/Weakly_detector/src/detector.py", line 10, in init with open(weight_file_path) as f: IOError: [Errno 2] No such file or directory: '../data/caffe_layers_value.pickle'

can you tell me how to slove this? thanks

I am using your technique to obtain activation maps for only one class in a binary classification problem. The network I have trained is inception v3. I cut the last 3 inception modules and then I added a new convolutional layer, a gap layer and a softmax. The input of the new convolutional layer has shape batch_size x 17 x 17 x 768 and the output's shape is batch_size x 17 x 17 x 1024, using 3 x 3 kernel. The precision of the network is high (89%). Do you have idea what might be wrong? I have not changed your technique.

I have trained inception v3 network and I have achieved high precision. I am cutting the last 3 inception modules of the network and the output of the last module is batch_size x 17 x 17 x 768. In the next steps I am using your technique to get activation map of each image but results are bad. The output classes are only 2 and I want to have right activation maps only for the one class. Can you tell me what am I missing? Here is the code:

` def conv2d(input_image, name): filter_shape = [3, 3, 768, 1024] with tf.name_scope('MyConv'): w = tf.Variable(tf.random_uniform(filter_shape, minval=0.0, maxval=0.01), name='weights') b = tf.Variable(tf.zeros(filter_shape[-1]), name='biases')

	conv = tf.nn.conv2d(input_image, w, [1,1,1,1], padding='SAME')
	outputs = tf.nn.bias_add(conv, b)

return outputs

`

`def add_final_training_ops(net, ground_truth_input, class_count, final_tensor_name):

# Convolutional layer
convoluted = conv2d(net, 'NewConvLayer')

# GAP layer
gap = tf.reduce_mean(convoluted, [1, 2])

# Organizing the following ops as `final_training_ops` so they're easier
# to see in TensorBoard
layer_name = 'final_training_ops'
with tf.name_scope(layer_name):
	with tf.name_scope('weights'):
		layer_weights = tf.Variable(tf.random_uniform([1024, class_count], minval=0.0, maxval=0.01),
													name='final_weights')

	with tf.name_scope('Wx_plus_b'):
		logits = tf.matmul(gap, layer_weights)

final_tensor = tf.nn.softmax(logits, name=final_tensor_name)

return final_tensor, layer_weights, convoluted`

`def heat_map(image, softmax_weights, labels): img_resize = tf.image.resize_bilinear(image, [299, 299]) with tf.variable_scope("GAP", reuse=True): label_w = tf.gather(tf.transpose(softmax_weights), labels) label_w = tf.reshape(label_w, [-1, 1024, 1]) # [batch_size, 1024, 1]

img_resize = tf.reshape(img_resize, [-1, 299*299, 1024]) # [batch_size, 299*299, 1024] activation = tf.batch_matmul(img_resize, label_w) activation = tf.reshape(activation, [-1, 299, 299]) return activation`

`final_tensor, softmax_weights, convoluted = add_final_training_ops(inception_7, labels, num_classes, 'final_result')

	prediction = tf.argmax(final_tensor, 1)
	activation_map = heat_map(convoluted, softmax_weights, labels)`

What I did so far:

1. I downloaded the caltech256 dataset from kaggle (link: https://www.kaggle.com/jessicali9530/caltech256)
2. put the zip into the folder (~/WeaklyDetector/data/)
3. extracted the zip and
4. adjusted the variable 'dataset_path' in train.caltech.py (line 19) to (~/WeaklyDetector/data/256_ObjectCategories)

As i run the train.caltech.py file, following error occured: ValueError: invalid literal for int() with base 10: ''

What the algorithm does:

The algorithm reads all foldernames in the path we previously set in step 4 and split the name by the delimiter '.'. The numbers are stored in the variable 'labels' and the name of the corresponding classes in 'label_names' (line 32). So the algorithm expects this folder only to contain folders with the scheme '[0-9]+.[A-z_-]+'. (in other words, there are some numbers, followed by a point and a name).

What raised the error and how to solve:

In the folder of step 4 there is a hidden folder, called '.DS_Store'. Apparently- this folder does not match the desired scheme. It seems to be necessary for mac users, though. With help of print commands i verified that the algorithm reads this folder. Splitting the name as described in the previous section leads to the empty label '' and the label_name 'DS_Store'. The algorithm tries to map the empty label to an int value, that is of course impossible.

Just remove that file and it should work. Or try to filter this specific file in the program.

I tried training a model on this but I keep getting 'Training Loss: nan' I trained for 17 epochs before I stopped because of this. Is it that the model is not learning or could it be a different issue?

@jazzsaxmafia Hello, :\Tesnsorflow\Weaklydetector\src>python train.caltech.py Traceback (most recent call last): File "train.caltech.py", line 44, in image_paths_train = np.hstack(map(lambda one_class: one_class[:-10], image_paths_per_label)) File "C:\Program Files\Anaconda3\lib\site-packages\numpy\core\shape_base.py", line 275, in hstack arrs = [atleast_1d(_m) for _m in tup] File "C:\Program Files\Anaconda3\lib\site-packages\numpy\core\shape_base.py", line 275, in arrs = [atleast_1d(_m) for _m in tup] File "train.caltech.py", line 44, in image_paths_train = np.hstack(map(lambda one_class: one_class[:-10], image_paths_per_label)) TypeError: 'map' object is not subscriptable

:\Tesnsorflow\Weaklydetector\src>python train.caltech.py Traceback (most recent call last): File "train.caltech.py", line 40, in image_paths_train = np.hstack(list(map(lambda one_class: one_class[:-10], image_paths_per_label))) File "train.caltech.py", line 40, in image_paths_train = np.hstack(list(map(lambda one_class: one_class[:-10], image_paths_per_label))) TypeError: 'map' object is not subscriptable

Any idea of this problem? Thank you very much.

A Jupyter notebook including the main files needed to train the VGG model on Caltech256 dataset. Notebook includes code updates (TF v1.2) and documentation, among others.