Agfdhyk

I'm trying to run code below to generate a JSON file and use it to built a t-SNE with a set of images. However my experience with Keras and machine learning is limited and I'm unable to run code below and getting error: AttributeError: 'Tensor' object has no attribute '_keras_shape'

import argparse

import sys

import numpy as np

import json

import os

from os.path import isfile, join

import keras

from keras.preprocessing import image

from keras.applications.imagenet_utils import decode_predictions, preprocess_input

from keras.models import Model

from sklearn.decomposition import PCA

from sklearn.manifold import TSNE

from scipy.spatial import distance



def process_arguments(args):

    parser = argparse.ArgumentParser(description='tSNE on audio')

    parser.add_argument('--images_path', action='store', help='path to directory of images')

    parser.add_argument('--output_path', action='store', help='path to where to put output json file')

    parser.add_argument('--num_dimensions', action='store', default=2, help='dimensionality of t-SNE points (default 2)')

    parser.add_argument('--perplexity', action='store', default=30, help='perplexity of t-SNE (default 30)')

    parser.add_argument('--learning_rate', action='store', default=150, help='learning rate of t-SNE (default 150)')

    params = vars(parser.parse_args(args))

    return params



def get_image(path, input_shape):

    img = image.load_img(path, target_size=input_shape)

    x = image.img_to_array(img)

    x = np.expand_dims(x, axis=0)

    x = preprocess_input(x)

    return x



def find_candidate_images(images_path):

    """

    Finds all candidate images in the given folder and its sub-folders.

    Returns:

        images: a list of absolute paths to the discovered images.

    """

    images = 

    for root, dirs, files in os.walk(images_path):

        for name in files:

            file_path = os.path.abspath(os.path.join(root, name))

            if ((os.path.splitext(name)[1]).lower() in ['.jpg','.png','.jpeg']):

                images.append(file_path)

    return images



def analyze_images(images_path):

    # make feature_extractor

    model = keras.applications.VGG16(weights='imagenet', include_top=True)

    feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)

    input_shape = model.input_shape[1:3]

    # get images

    candidate_images = find_candidate_images(images_path)

    # analyze images and grab activations

    activations = 

    images = 

    for idx,image_path in enumerate(candidate_images):

        file_path = join(images_path,image_path)

        img = get_image(file_path, input_shape);

        if img is not None:

            print("getting activations for %s %d/%d" % (image_path,idx,len(candidate_images)))

            acts = feat_extractor.predict(img)[0]

            activations.append(acts)

            images.append(image_path)

    # run PCA firt

    print("Running PCA on %d images..." % len(activations))

    features = np.array(activations)

    pca = PCA(n_components=300)

    pca.fit(features)

    pca_features = pca.transform(features)

    return images, pca_features



def run_tsne(images_path, output_path, tsne_dimensions, tsne_perplexity, tsne_learning_rate):

    images, pca_features = analyze_images(images_path)

    print("Running t-SNE on %d images..." % len(images))

    X = np.array(pca_features)

    tsne = TSNE(n_components=tsne_dimensions, learning_rate=tsne_learning_rate, perplexity=tsne_perplexity, verbose=2).fit_transform(X)

    # save data to json

    data = 

    for i,f in enumerate(images):

        point = [float((tsne[i,k] - np.min(tsne[:,k]))/(np.max(tsne[:,k]) - np.min(tsne[:,k]))) for k in range(tsne_dimensions) ]

        data.append({"path":os.path.abspath(join(images_path,images[i])), "point":point})

    with open(output_path, 'w') as outfile:

        json.dump(data, outfile)





if __name__ == '__main__':

    params = process_arguments(sys.argv[1:])

    images_path = params['images_path']

    output_path = params['output_path']

    tsne_dimensions = int(params['num_dimensions'])

    tsne_perplexity = int(params['perplexity'])

    tsne_learning_rate = int(params['learning_rate'])

    run_tsne(images_path, output_path, tsne_dimensions, tsne_perplexity, tsne_learning_rate)

    print("finished saving %s" % output_path)

from: https://github.com/ml4a/ml4a-ofx/blob/master/scripts/tSNE-images.py

Here is what I'm getting:

    Traceback (most recent call last):

  File "tSNE-images.py", line 95, in <module>

    run_tsne(images_path, output_path, tsne_dimensions, tsne_perplexity, tsne_learning_rate)

  File "tSNE-images.py", line 75, in run_tsne

    images, pca_features = analyze_images(images_path)

  File "tSNE-images.py", line 50, in analyze_images

    feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)

  File "/Users/.../anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper

    return func(*args, **kwargs)

  File "/Users/.../anaconda3/lib/python3.6/site-packages/keras/engine/network.py", line 91, in __init__

    self._init_graph_network(*args, **kwargs)

  File "/Users/.../anaconda3/lib/python3.6/site-packages/keras/engine/network.py", line 251, in _init_graph_network

    input_shapes=[x._keras_shape for x in self.inputs],

  File "/Users/.../anaconda3/lib/python3.6/site-packages/keras/engine/network.py", line 251, in <listcomp>

    input_shapes=[x._keras_shape for x in self.inputs],

AttributeError: 'Tensor' object has no attribute '_keras_shape'

I found similar error in here:

`https://stackoverflow.com/questions/47616588/keras-throws-tensor-object-has-no-attribute-keras-shape-when-splitting-a`

However I can't seem to figure out how to go about updating code using Lambda. How can I solve this error?

I followed @user2300867 suggestion and updated tensorflow with:

pip3 install --upgrade tensorflow-gpu

and updated keras to 2.2.4

pip install Keras==2.2.4

I still got error:

TypeError: expected str, bytes or os.PathLike object, not NoneType

but this was easy to fix by simply editing the code for local paths