def is_hot_dog(preds):
decoded = decode_predictions(preds, top=1)
# pull out predicted label, which is in d[0][1] due to how decode_predictions structures results
labels = [d[0][1] for d in decoded]
out = [l == 'hotdog' for l in labels]
return out
def is_hot_dog(preds):
'''
inputs:
preds_array: array of predictions from pre-trained model
outputs:
is_hot_dog_list: a list indicating which predictions show hotdog as the most likely label
'''
decoded = decode_predictions(preds, top=1)
# pull out predicted label, which is in d[0][1] due to how decode_predictions structures results
labels = [d[0][1] for d in decoded]
out = [l == 'hotdog' for l in labels]
return out
img_width=image_size):
imgs = [
load_img(img_path, target_size=(img_height, img_width))
for img_path in img_paths
]
img_array = np.array([img_to_array(img) for img in imgs])
output = preprocess_input(img_array)
return (output)
my_model = ResNet50(
weights='../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels.h5')
test_data = read_and_prep_images(img_paths)
preds = my_model.predict(test_data)
most_likely_labels = decode_predictions(preds, top=3)
for i, img_path in enumerate(img_paths):
display(Image(img_path))
print(most_likely_labels[i])
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.deep_learning.exercise_3 import *
print("Setup Complete")
# Experiment with code outside the function, then move it into the function once you think it is right
# the following lines are given as a hint to get you started
decoded = decode_predictions(preds, top=1)
def read_and_prep_images(img_paths,
img_height=image_size,
img_width=image_size):
imgs = [
load_img(img_path, target_size=(img_height, img_width))
for img_path in img_paths
]
img_array = np.array([img_to_array(img) for img in imgs])
output = preprocess_input(img_array)
return (output)
from tensorflow.python.keras.applications import ResNet50
my_model = ResNet50(
weights='../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels.h5')
test_data = read_and_prep_images(img_paths)
preds = my_model.predict(test_data)
from learntools.deep_learning.decode_predictions import decode_predictions
from IPython.display import Image, display
most_likely_labels = decode_predictions(
preds,
top=3,
class_list_path='../input/resnet50/imagenet_class_index.json')
for i, img_path in enumerate(img_paths):
display(Image(img_path))
print(most_likely_labels[i])
img = [
load_img(img_path, target_size=(img_height, img_width))
for img_path in img_paths
]
img_array = np.array([img_to_array(img) for img in imgs])
output = preprocess_output(img_array)
return (output)
from tensorflow.python.keras.applications import ResNet50
my_model = ResNet50(
weights=
'Desktop/Kartikey DS/Deep Learning/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
)
test_data = read_and_prep_images(img_paths)
preds = my_model.predict(test_data)
from learntools.deep_learning.decode_predictions import decode_predictions
from IPython.display import Image, display
most_likely_labels = decode_predictions(
preds,
top=3,
class_list_path=
'Desktop/Kartikey DS/Deep Learning/imagenet_class_index.json')
for i, img_path in enumerate(img_paths):
display(Image(img_path))
print(most_likely_labels[i])