| A Secret | |
| alter ego | |
| angels & spirits | |
| another lazy summer | |
| apophenia | |
| apple | |
| Archaeology | |
| as a treat | |
| B movies | |
| bad poetry |
| about: Catalog of virtual products loading, normalising and cloud masking Landsat products | |
| products: | |
| # Loads a subset of data from Landsat 8, including spectral bands | |
| # (blue, green, red, nir, swir1, swir2), a cloud masking band (fmask) | |
| # and a contiguity band (contiguity). | |
| ls8_nbart: | |
| recipe: | |
| &ls8_nbart | |
| product: ga_ls8c_ard_3 |
Should be work with 0.18
Destructuring(or pattern matching) is a way used to extract data from a data structure(tuple, list, record) that mirros the construction. Compare to other languages, Elm support much less destructuring but let's see what it got !
myTuple = ("A", "B", "C")
myNestedTuple = ("A", "B", "C", ("X", "Y", "Z"))
| import PIL.Image | |
| from cStringIO import StringIO | |
| import IPython.display | |
| import numpy as np | |
| def showarray(a, fmt='png'): | |
| a = np.uint8(a) | |
| f = StringIO() | |
| PIL.Image.fromarray(a).save(f, fmt) | |
| IPython.display.display(IPython.display.Image(data=f.getvalue())) |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |