我试图运行下面的代码生成一个JSON文件，并使用它来构建一组图像的t-SNE。然而，我的Keras和机器学习经验有限，我无法运行下面的代码，出现错误：AttributeError：'Tensor'对象没有属性'_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)
来源：https://github.com/ml4a/ml4a-ofx/blob/master/scripts/tSNE-images.py
这是我得到的：
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'
我在这里找到了类似的错误：
`https://stackoverflow.com/questions/47616588/keras-throws-tensor-object-has-no-attribute-keras-shape-when-splitting-a`
然而，我似乎无法弄清楚如何使用Lambda更新代码。我该如何解决这个错误？