A simple ipython notebook that walks through the creation of a softmax regression model using MNIST dataset.

softmax.ipynb

Hi,
unfortuately the code doesn't work for me.
The main loop [in 11] produces the folowing error: ValueError: row, column, and data arrays must be 1-D
(\python-3.5.2.amd64\lib\site-packages\scipy\sparse\coo.py in getnnz(self, axis)): line 195

any solution?

Cheers
Rolf

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