Created
June 2, 2017 06:34
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from keras.models import load_model | |
from keras.models import Sequential | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras.layers import Activation, Dropout, Flatten, Dense | |
from keras.preprocessing.image import img_to_array, load_img | |
import numpy as np | |
import tensorflow as tf | |
# dimensions of our images. | |
img_width, img_height = 150, 150 | |
input_shape = (img_width, img_height, 3) | |
test_model = Sequential() | |
test_model.add(Conv2D(32, (3, 3), input_shape=input_shape)) | |
test_model.add(Activation('relu')) | |
test_model.add(MaxPooling2D(pool_size=(2, 2))) | |
test_model.add(Conv2D(32, (3, 3))) | |
test_model.add(Activation('relu')) | |
test_model.add(MaxPooling2D(pool_size=(2, 2))) | |
test_model.add(Conv2D(64, (3, 3))) | |
test_model.add(Activation('relu')) | |
test_model.add(MaxPooling2D(pool_size=(2, 2))) | |
test_model.add(Flatten()) | |
test_model.add(Dense(64)) | |
test_model.add(Activation('relu')) | |
test_model.add(Dropout(0.5)) | |
test_model.add(Dense(1)) | |
test_model.add(Activation('sigmoid')) | |
test_model = load_model('first_model.h5') | |
def predict(basedir, model): | |
for i in range(1401,1411): | |
path = basedir + str(i) + '.jpg' | |
img = load_img(path,False,target_size=(img_width,img_height)) | |
x = img_to_array(img) | |
x = np.expand_dims(x, axis=0) | |
preds = model.predict_classes(x) | |
probs = model.predict_proba(x) | |
print(probs) | |
basedir = "data/test/cat." | |
predict(basedir, test_model) | |
basedir = "data/test/dog." | |
predict(basedir, test_model) | |
print('done') | |
hello I am Trying to use the classificator, but in the last part I get the follow error: predict() fp = builtins.open(filename, "rb") PermissionError: [Errno 13] Permission denied:
Some recommendation for the path route?, I found that this error could be admin permision, but I don't know...
I appreciate your help,
Best regards,
RM
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Hi, I would add a change to your code by adding intensity normalization for test images.
img = load_img(path,False,target_size=(img_width,img_height))
x = img_to_array(img)
x= x / 255
x = np.expand_dims(x, axis=0)
The training images had this normalisation, therefore the test images should go through it too.
Thanks for the code.