import numpy as np
from keras.preprocessing import image
from keras.applications import resnet50
C:\Users\rstancut\AppData\Local\Continuum\anaconda2\envs\keras\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend.
from keras import backend as K
print(K.backend())
tensorflow
# load pre-trained model
model = resnet50.ResNet50()
WARNING:tensorflow:From C:\Users\rstancut\AppData\Local\Continuum\anaconda2\envs\keras\lib\site-packages\keras\backend\tensorflow_backend.py:1255: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead
# load and resixe image to match model nodes
img = image.load_img("Exercise Files/05/bay.jpg", target_size=(224, 224))
# convert image to np array
x = image.img_to_array(img)
# the model expects multiple images, a list
x = np.expand_dims(x, axis=0)
# scale input image to range used in trained NN
x = resnet50.preprocess_input(x)
# run image through NN to make a predict
predictions = model.predict(x)
# look up predicted class names
# returns top 5
# add ", top=n" for less/more results in `decode_predictions` arguments
predicted_classes = resnet50.decode_predictions(predictions)
Downloading data from https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json 40960/35363 [==================================] - 0s 4us/step
print("This is an image of:")
for imagenet_id, name, likelihood in predicted_classes[0]:
print(" - {}: {:2f} likelihood".format(name, likelihood))
This is an image of: - lakeside: 0.372070 likelihood - seashore: 0.301635 likelihood - dock: 0.143978 likelihood - breakwater: 0.056647 likelihood - promontory: 0.042953 likelihood