Last active
December 27, 2024 03:41
-
-
Save awjuliani/5ce098b4b76244b7a9e3 to your computer and use it in GitHub Desktop.
A simple ipython notebook that walks through the creation of a softmax regression model using MNIST dataset.
I have a working code here :
https://github.com/arjunprakash027/HandcraftedML/blob/main/ground_ups/softmax.ipynb
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
I am working image caption generator project. now I am getting this kind of error in the testing phase.... hope someone will help me resolve this error.... THANK

YOU!!!
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help="C:\Users\Dell\anaconda3\envs\testenv\Flickr8k_Dataset\Flicker8k_Dataset\667626_18933d713e.jpg")
args = vars(ap.parse_args())
img_path = args['image']
def extract_features(filename, model):
try:
image = Image.open(filename)
except:
print("ERROR: Couldn't open image! Make sure the image path and extension is correct")
image = image.resize((299,299))
image = np.array(image)
# for images that has 4 channels, we convert them into 3 channels
if image.shape[2] == 4:
image = image[..., :3]
image = np.expand_dims(image, axis=0)
image = image/127.5
image = image - 1.0
feature = model.predict(image)
return feature
def word_for_id(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def generate_desc(model, tokenizer, photo, max_length):
in_text = 'start'
for i in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length)
pred = model.predict([photo,sequence], verbose=0)
pred = np.argmax(pred)
word = word_for_id(pred, tokenizer)
if word is None:
break
in_text += ' ' + word
if word == 'end':
break
return in_text
#path = 'Flicker8k_Dataset/111537222_07e56d5a30.jpg'
max_length = 32
tokenizer = load(open("tokenizer.p","rb"))
model = load_model('models/model_9.h5')
xception_model = Xception(include_top=False, pooling="avg")
photo = extract_features(img_path, xception_model)
img = Image.open(img_path)
description = generate_desc(model, tokenizer, photo, max_length)
print("\n\n")
print(description)
plt.imshow(img)
usage: [-h] -i IMAGE
: error: the following arguments are required: -i/--image
An exception has occurred, use %tb to see the full traceback.
SystemExit: 2