model = Net(weights="imagenet", input_shape=(img_height, img_height, 3))
# Load the image for prediction.
img = image.load_img(img_path, target_size=(img_height, img_height))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print("Predicted:", decode_predictions(preds, top=3)[0])
# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]
# Save the h5 file to path specified.
model.save(model_fname)
# ## Benchmark Keras prediction speed.
# In[2]:
import time
times = []
for i in range(20):
start_time = time.time()
preds = model.predict(x)
delta = time.time() - start_time
times.append(delta)
mean_delta = np.array(times).mean()
fps = 1 / mean_delta
