print('epoch %4d, cost = %.9f' % (epoch, avg_cost))
print("Accuracy: %f" % acc.eval(session=sess,
feed_dict={
x: mnist.test.images,
y: mnist.test.labels
}))
summary_writer.close()
### Examine layers
# A red/black/blue colormap
cdict = {
'red': [(0.0, 1.0, 1.0), (0.25, 1.0, 1.0), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0)],
'green': [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0)],
'blue': [(0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)]
}
redblue = matplotlib.colors.LinearSegmentedColormap('red_black_blue', cdict,
256)
wts = W.eval(sess)
for i in range(0, 5):
im = wts.flatten()[i::10].reshape((28, -1))
plt.imshow(im, cmap=redblue, clim=(-1.0, 1.0))
plt.colorbar()
print("Digit %d" % i)
plt.show()
### Explore using Tensorboard
utils.start_tensorboard(logs_path)
summary_writer.close()
### Examine layers
# A red/black/blue colormap
cdict = {
'red': [(0.0, 1.0, 1.0), (0.25, 1.0, 1.0), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0)],
'green': [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0)],
'blue': [(0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)]
}
redblue = matplotlib.colors.LinearSegmentedColormap('red_black_blue', cdict,
256)
wts = W.eval(sess)
for i in range(0, 5):
im = wts.flatten()[i::10].reshape((28, -1))
plt.imshow(im, cmap=redblue, clim=(-1.0, 1.0))
plt.colorbar()
print("Digit %d" % i)
plt.show()
### Explore using Tensorboard
utils.start_tensorboard(logs_path, iframe=False)
# Script completed! Click on the above link!
# ======================
#
# This example code showcases:
# - Ability to install and use custom packages (e.g. `pip search tensorflow`)
import utils
init_engine()
##### 1. __define location of Inceptionv3 frozen model,the graph input/output names and labels.__
inputs = ['input']
outputs = ['InceptionV3/Predictions/Reshape_1']
tf_model = '/home/cdsw/jumpstart/frozen_models/inception_v3_2016_08_28_frozen.pb'
tf_labels = "/home/cdsw/jumpstart/frozen_models/imagenet_slim_labels.txt"
##### 2. __Load the tensorflow model as an BigDL model__
model = Model.load_tensorflow(tf_model, inputs, outputs, bigdl_type="float")
##### 3. __(Optional) save it in a folder so that it can be viewed using tensorboard __
model.save_graph_topology("/tmp/bigdl_summaries/")
utils.start_tensorboard("/tmp/bigdl_summaries/")
##### should be able to select tensorboard from the cdsw project menu then graphs from the tensorboard menu
Image('images/bigdl_tensorflow_imported_graphview.png')
with open(tf_labels,"r") as lbls:
label_lines = [line.rstrip() for line in lbls]
testset = {'lemon':'lemon.jpg','squirrel':'squirrel.jpg',"basketball game":"basketball_game.jpg"}
predicted =[]
for (lbl,imgpath) in testset.iteritems():
test_image = image.load_img(imgpath,target_size=(299,299))
test_image = image.img_to_array(test_image)
x = np.expand_dims(test_image, axis=0)
x = preprocess_input(x)
res = model.predict(x)
reslbl = { label_lines[k]:v for k,v in enumerate(res[0]) }