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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