markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Bootstrap Estimate the standard error of $\hat{p}$ using bootstrap. | def bootstrap_se_est(y, stat_function, B=1000):
# 1. Generate bootstrap samples from the observed/simulated data (i.e. y)
# 2. Compute the statistic (using stat_function passed) on the bootstrap
# samples
# 3. Compute the standard error -> std dev
t_boot_list = [stat_function(np.random.choice(y, len(y), replace=True))
for _ in range(B)]
return np.std(t_boot_list)
print("Standard error of p_hat computed by bootstrap:")
print(bootstrap_se_est(y, estimator)) | Standard error of p_hat computed by bootstrap:
0.04889048066853097
| MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
Validate the estimated standard error by computing it analytically. | def estimator_analytical_se(p, n):
return np.sqrt(p * (1-p) / n)
print("Analytical standard error for the estimator: ", estimator_analytical_se(p, n)) | Analytical standard error for the estimator: 0.04898979485566356
| MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
Estimate the 95% confidence interval for $p$. | def confidence_interval_95_for_p(y):
ci_lower = estimator(y) - 1.96*bootstrap_se_est(y, estimator)
ci_higher = estimator(y) + 1.96*bootstrap_se_est(y, estimator)
return (ci_lower, ci_higher)
lower, higher = confidence_interval_95_for_p(y)
print("95% confidence interval for p: ({},{})".format(lower, higher)) | 95% confidence interval for p: (0.5254445619916019,0.717033857596202)
| MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
Validate the 95% confidence interval for $p$. | ci_contains_p_flags = []
for sim in range(1000):
y = np.random.binomial(n=1, p=p, size=n)
ci_lower, ci_higher = confidence_interval_95_for_p(y)
if ci_lower < p and p < ci_higher:
ci_contains_p_flags.append(1)
else:
ci_contains_p_flags.append(0)
coverage = np.mean(ci_contains_p_flags)
print("Coverage of 95% confidence interval for p: ", coverage) | Coverage of 95% confidence interval for p: 0.93
| MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
Bayesian Inference **[Optional]**Estimate $p$ using Bayesian inference. As the prior for $p$ use Normal(0.5, 0.1). | !pip install pystan
import pystan
model_code = """
data {
int<lower=0> n;
int<lower=0,upper=1> y[n];
}
parameters {
real<lower=0,upper=1> p;
}
model {
p ~ normal(0.5, 0.1);
for (i in 1:n)
y[i] ~ bernoulli(p);
}
"""
model = pystan.StanModel(model_code=model_code)
fit = model.sampling(data={"n": n, "y": y}, iter=2000, chains=4, n_jobs=4)
print(fit.stansummary()) | _____no_output_____ | MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
Compute the Bayesian inference results if our data contains 20 coin tosses instead. | n = 20
p = 0.6
y = np.random.binomial(1, p, n)
model = pystan.StanModel(model_code=model_code)
fit = model.sampling(data={"n": n, "y": y}, iter=2000, chains=4, n_jobs=4)
print(fit.stansummary()) | _____no_output_____ | MIT | colab/lab_1_statistical_inference_cmunoz.ipynb | cmunozcortes/ds-fundamentals |
XResNet baseline | #https://github.com/fastai/fastai_docs/blob/master/dev_course/dl2/11_train_imagenette.ipynb
def noop(x): return x
class Flatten(nn.Module):
def forward(self, x): return x.view(x.size(0), -1)
def conv(ni, nf, ks=3, stride=1, bias=False):
return nn.Conv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=bias)
act_fn = nn.ReLU(inplace=True)
def init_cnn(m):
if getattr(m, 'bias', None) is not None: nn.init.constant_(m.bias, 0)
if isinstance(m, (nn.Conv2d,nn.Linear)): nn.init.kaiming_normal_(m.weight)
for l in m.children(): init_cnn(l)
def conv_layer(ni, nf, ks=3, stride=1, zero_bn=False, act=True):
bn = nn.BatchNorm2d(nf)
nn.init.constant_(bn.weight, 0. if zero_bn else 1.)
layers = [conv(ni, nf, ks, stride=stride), bn]
if act: layers.append(act_fn)
return nn.Sequential(*layers)
class ResBlock(nn.Module):
def __init__(self, expansion, ni, nh, stride=1):
super().__init__()
nf,ni = nh*expansion,ni*expansion
layers = [conv_layer(ni, nh, 3, stride=stride),
conv_layer(nh, nf, 3, zero_bn=True, act=False)
] if expansion == 1 else [
conv_layer(ni, nh, 1),
conv_layer(nh, nh, 3, stride=stride),
conv_layer(nh, nf, 1, zero_bn=True, act=False)
]
self.convs = nn.Sequential(*layers)
self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)
self.pool = noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)
def forward(self, x): return act_fn(self.convs(x) + self.idconv(self.pool(x)))
class XResNet(nn.Sequential):
@classmethod
def create(cls, expansion, layers, c_in=3, c_out=1000):
nfs = [c_in, (c_in+1)*8, 64, 64]
stem = [conv_layer(nfs[i], nfs[i+1], stride=2 if i==0 else 1)
for i in range(3)]
nfs = [64//expansion,64,128,256,512]
res_layers = [cls._make_layer(expansion, nfs[i], nfs[i+1],
n_blocks=l, stride=1 if i==0 else 2)
for i,l in enumerate(layers)]
res = cls(
*stem,
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
*res_layers,
nn.AdaptiveAvgPool2d(1), Flatten(),
nn.Linear(nfs[-1]*expansion, c_out),
)
init_cnn(res)
return res
@staticmethod
def _make_layer(expansion, ni, nf, n_blocks, stride):
return nn.Sequential(
*[ResBlock(expansion, ni if i==0 else nf, nf, stride if i==0 else 1)
for i in range(n_blocks)])
def xresnet18 (**kwargs): return XResNet.create(1, [2, 2, 2, 2], **kwargs)
def xresnet34 (**kwargs): return XResNet.create(1, [3, 4, 6, 3], **kwargs)
def xresnet50 (**kwargs): return XResNet.create(4, [3, 4, 6, 3], **kwargs)
def xresnet101(**kwargs): return XResNet.create(4, [3, 4, 23, 3], **kwargs)
def xresnet152(**kwargs): return XResNet.create(4, [3, 8, 36, 3], **kwargs) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
XResNet with Self Attention | #Unmodified from https://github.com/fastai/fastai/blob/5c51f9eabf76853a89a9bc5741804d2ed4407e49/fastai/layers.py
def conv1d(ni:int, no:int, ks:int=1, stride:int=1, padding:int=0, bias:bool=False):
"Create and initialize a `nn.Conv1d` layer with spectral normalization."
conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias=bias)
nn.init.kaiming_normal_(conv.weight)
if bias: conv.bias.data.zero_()
return spectral_norm(conv)
# Adapted from SelfAttention layer at https://github.com/fastai/fastai/blob/5c51f9eabf76853a89a9bc5741804d2ed4407e49/fastai/layers.py
# Inspired by https://arxiv.org/pdf/1805.08318.pdf
class SimpleSelfAttention(nn.Module):
def __init__(self, n_in:int, ks=1):#, n_out:int):
super().__init__()
self.conv = conv1d(n_in, n_in, ks, padding=ks//2, bias=False)
self.gamma = nn.Parameter(tensor([0.]))
def forward(self,x):
size = x.size()
x = x.view(*size[:2],-1)
o = torch.bmm(x.permute(0,2,1).contiguous(),self.conv(x))
o = self.gamma * torch.bmm(x,o) + x
return o.view(*size).contiguous()
#unmodified from https://github.com/fastai/fastai/blob/9b9014b8967186dc70c65ca7dcddca1a1232d99d/fastai/vision/models/xresnet.py
def conv(ni, nf, ks=3, stride=1, bias=False):
return nn.Conv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=bias)
def noop(x): return x
def conv_layer(ni, nf, ks=3, stride=1, zero_bn=False, act=True):
bn = nn.BatchNorm2d(nf)
nn.init.constant_(bn.weight, 0. if zero_bn else 1.)
layers = [conv(ni, nf, ks, stride=stride), bn]
if act: layers.append(act_fn)
return nn.Sequential(*layers)
# Modified from https://github.com/fastai/fastai/blob/9b9014b8967186dc70c65ca7dcddca1a1232d99d/fastai/vision/models/xresnet.py
# Added self attention
class ResBlock(nn.Module):
def __init__(self, expansion, ni, nh, stride=1,sa=False):
super().__init__()
nf,ni = nh*expansion,ni*expansion
layers = [conv_layer(ni, nh, 3, stride=stride),
conv_layer(nh, nf, 3, zero_bn=True, act=False)
] if expansion == 1 else [
conv_layer(ni, nh, 1),
conv_layer(nh, nh, 3, stride=stride),
conv_layer(nh, nf, 1, zero_bn=True, act=False)
]
self.sa = SimpleSelfAttention(nf,ks=1) if sa else noop
self.convs = nn.Sequential(*layers)
self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)
self.pool = noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)
def forward(self, x):
return act_fn(self.sa(self.convs(x)) + self.idconv(self.pool(x)))
# Modified from https://github.com/fastai/fastai/blob/9b9014b8967186dc70c65ca7dcddca1a1232d99d/fastai/vision/models/xresnet.py
# Added self attention
class XResNet_sa(nn.Sequential):
@classmethod
def create(cls, expansion, layers, c_in=3, c_out=1000):
nfs = [c_in, (c_in+1)*8, 64, 64]
stem = [conv_layer(nfs[i], nfs[i+1], stride=2 if i==0 else 1)
for i in range(3)]
nfs = [64//expansion,64,128,256,512]
res_layers = [cls._make_layer(expansion, nfs[i], nfs[i+1],
n_blocks=l, stride=1 if i==0 else 2, sa = True if i in[len(layers)-4] else False)
for i,l in enumerate(layers)]
res = cls(
*stem,
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
*res_layers,
nn.AdaptiveAvgPool2d(1), Flatten(),
nn.Linear(nfs[-1]*expansion, c_out),
)
init_cnn(res)
return res
@staticmethod
def _make_layer(expansion, ni, nf, n_blocks, stride, sa = False):
return nn.Sequential(
*[ResBlock(expansion, ni if i==0 else nf, nf, stride if i==0 else 1, sa if i in [n_blocks -1] else False)
for i in range(n_blocks)])
def xresnet50_sa (**kwargs): return XResNet_sa.create(4, [3, 4, 6, 3], **kwargs) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Data loading | #https://github.com/fastai/fastai/blob/master/examples/train_imagenette.py
def get_data(size, woof, bs, workers=None):
if size<=128: path = URLs.IMAGEWOOF_160 if woof else URLs.IMAGENETTE_160
elif size<=224: path = URLs.IMAGEWOOF_320 if woof else URLs.IMAGENETTE_320
else : path = URLs.IMAGEWOOF if woof else URLs.IMAGENETTE
path = untar_data(path)
n_gpus = num_distrib() or 1
if workers is None: workers = min(8, num_cpus()//n_gpus)
return (ImageList.from_folder(path).split_by_folder(valid='val')
.label_from_folder().transform(([flip_lr(p=0.5)], []), size=size)
.databunch(bs=bs, num_workers=workers)
.presize(size, scale=(0.35,1))
.normalize(imagenet_stats)) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Train | opt_func = partial(optim.Adam, betas=(0.9,0.99), eps=1e-6) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Imagewoof Image size = 256 | image_size = 256
data = get_data(image_size,woof =True,bs=64) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Epochs = 5 | # we use the same parameters for baseline and new model
epochs = 5
lr = 3e-3
bs = 64
mixup = 0 | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Baseline | m = xresnet50(c_out=10)
learn = (Learner(data, m, wd=1e-2, opt_func=opt_func,
metrics=[accuracy,top_k_accuracy],
bn_wd=False, true_wd=True,
loss_func = LabelSmoothingCrossEntropy())
)
if mixup: learn = learn.mixup(alpha=mixup)
learn = learn.to_fp16(dynamic=True)
learn.fit_one_cycle(epochs, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(epochs, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(epochs, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(epochs, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(epochs, lr, div_factor=10, pct_start=0.3)
results = [61.8,64.8,57.4,62.4,63,61.8, 57.6,63,62.6, 64.8] #included some from previous notebook iteration
np.mean(results), np.std(results), np.min(results), np.max(results) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
New model | m = xresnet50_sa(c_out=10)
learn = None
gc.collect()
learn = (Learner(data, m, wd=1e-2, opt_func=opt_func,
metrics=[accuracy,top_k_accuracy],
bn_wd=False, true_wd=True,
loss_func = LabelSmoothingCrossEntropy())
)
if mixup: learn = learn.mixup(alpha=mixup)
learn = learn.to_fp16(dynamic=True)
learn.fit_one_cycle(5, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(5, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(5, lr, div_factor=10, pct_start=0.3)
learn.fit_one_cycle(5, lr, div_factor=10, pct_start=0.3)
results = [67.4,65.8,70.6,65.8,67.8,69,65.6,66.4, 67.8,70.2]
np.mean(results), np.std(results), np.min(results), np.max(results) | _____no_output_____ | Apache-2.0 | Imagenette Simple Self Attention.ipynb | RubensZimbres/SimpleSelfAttention |
Software License Agreement (MIT License) Copyright (c) 2020, Amirhossein Pakdaman. Simple DFS, BFS **Problem**: Implement a search tree with the following characteristics:1. The initial state contains value 10.2. At each step two successors are created, the value one of them is one unit smaller than its parent and the other is one unit larger.3. Search tree continues up to 3 levels of depth. | import IPython
IPython.core.display.Image("tree.png", embed=True) | _____no_output_____ | MIT | BFS_DFS_simple_example/BFS_DFS_simple_example.ipynb | amirhpd/Python_Basics |
BFS | import queue
class Node:
def __init__(self,value,parent,depth):
self.value = value
self.parent = parent
self.depth = depth
parent = Node(10,None,0)
frontier = queue.Queue()
frontier.put(parent)
while frontier:
current_node = frontier.get()
if current_node.depth > 3:
break
frontier.put(Node(current_node.value-1, current_node, current_node.depth+1))
frontier.put(Node(current_node.value+1, current_node, current_node.depth+1))
print(current_node.value) | 10
9
11
8
10
10
12
7
9
9
11
9
11
11
13
| MIT | BFS_DFS_simple_example/BFS_DFS_simple_example.ipynb | amirhpd/Python_Basics |
DFS | class Node:
def __init__(self,value,parent,depth):
self.value = value
self.parent = parent
self.depth = depth
parent = Node(10,None,0)
frontier = []
frontier.append(parent)
while frontier:
current_node = frontier.pop()
if current_node.depth > 3:
current_node = frontier.pop()
if len(frontier) == 0:
break
current_node = frontier.pop()
frontier.append(Node(current_node.value+1, current_node, current_node.depth+1))
frontier.append(Node(current_node.value-1, current_node, current_node.depth+1))
print(current_node.value)
| 10
9
8
7
9
10
9
11
11
10
9
11
12
11
13
| MIT | BFS_DFS_simple_example/BFS_DFS_simple_example.ipynb | amirhpd/Python_Basics |
FunctionsLet's say that we have some code that does some task, but the code is 25 lines long, we need to run it over 1000 items and it doesn't work in a loop. How in the world will we handle this situation? That is where functions come in really handy. Functions are a generalized block of code that allow you to run code over and over while changing its parameters if you so choose. Functions may take **(arguments)** that you are allowed to change when you call the function. It may also **return** a value.A function must be defined before you can call it. To define a function, we use the following syntax: def (arg0, arg1, arg3,...): code here must be indented. you can use arg0,...,argn within the function you can also return things return 1 This code returns 1 no matter what you tell the function Functions can take as many arguments as you wish, but they may only return 1 thing. A simple example of a familiar function is any mathematical function. Take sin(x), it is a function that takes one argument x and returns one value based on the input. Let's get familiar with functions. | def add1(x):
return x+1
print(add1(1))
def xsq(x):
return x**2
print(xsq(5))
for i in range(0,10):
print(xsq(i)) | 2
25
0
1
4
9
16
25
36
49
64
81
| MIT | Python Workshop/Functions.ipynb | CalPolyPat/Python-Workshop |
The true power of functions is being able to call it as many times as we would like. In the previous example, we called the square function, xsq in a loop 10 times. Let's check out some more complicated examples. | def removefs(data):
newdata=''
for d in data:
if(d=="f" or d=="F"):
pass
else:
newdata+=(d)
return newdata
print(removefs('ffffffFFFFFg'))
intro='''##Functions
Let's say that we have some code that does some task, but the code is 25 lines long, we need to run it over 1000 items and it doesn't work in a loop. How in the world will we handle this situation? That is where functions come in really handy. Functions are a generalized block of code that allow you to run code over and over while changing its parameters if you so choose. Functions may take **(arguments)** that you are allowed to change when you call the function. It may also **return** a value.
A function must be defined before you can call it. To define a function, we use the following syntax:
def <function name>(arg0, arg1, arg3,...):
#code here must be indented.
#you can use arg0,...,argn within the function
#you can also return things
return 1
#This code returns 1 no matter what you tell the function
Functions can take as many arguments as you wish, but they may only return 1 thing. A simple example of a familiar function is any mathematical function. Take sin(x), it is a function that takes one argument x and returns one value based on the input. Let's get familiar with functions."'''
print(removefs(intro))
def removevowels(data):
newdata = ''
for d in data:
if(d=='a' or d=='e' or d=='i' or d=='o' or d=='u' or d=='y'):
pass
else:
newdata+=d
return newdata
print(removevowels(intro)) | ##Fnctns
Lt's s tht w hv sm cd tht ds sm tsk, bt th cd s 25 lns lng, w nd t rn t vr 1000 tms nd t dsn't wrk n lp. Hw n th wrld wll w hndl ths sttn? Tht s whr fnctns cm n rll hnd. Fnctns r gnrlzd blck f cd tht llw t rn cd vr nd vr whl chngng ts prmtrs f s chs. Fnctns m tk **(rgmnts)** tht r llwd t chng whn cll th fnctn. It m ls **rtrn** vl.
A fnctn mst b dfnd bfr cn cll t. T dfn fnctn, w s th fllwng sntx:
df <fnctn nm>(rg0, rg1, rg3,...):
#cd hr mst b ndntd.
# cn s rg0,...,rgn wthn th fnctn
# cn ls rtrn thngs
rtrn 1
#Ths cd rtrns 1 n mttr wht tll th fnctn
Fnctns cn tk s mn rgmnts s wsh, bt th m nl rtrn 1 thng. A smpl xmpl f fmlr fnctn s n mthmtcl fnctn. Tk sn(x), t s fnctn tht tks n rgmnt x nd rtrns n vl bsd n th npt. Lt's gt fmlr wth fnctns."
| MIT | Python Workshop/Functions.ipynb | CalPolyPat/Python-Workshop |
So clearly we can do some powerful things. Now let's see why these functions have significant power over loops. | def fib(n):
a,b = 1,1
for i in range(n-1):
a,b = b,a+b
return a
def printfib(n):
for i in range(0,n):
print(fib(i))
printfib(15) | 1
1
1
2
3
5
8
13
21
34
55
89
144
233
377
| MIT | Python Workshop/Functions.ipynb | CalPolyPat/Python-Workshop |
Here, using loops within functions allows to generate the fibonacci sequence. We then write a function to print out the first n numbers. Exercises1. Write a function that takes two arguments and returns a value that uses the arguments.2. Write a power function. It should take two arguments and returns the first argument to the power of the second argument.3. is a semi-guided exercise. If you are stumped ask for help.3a. Write a function that takes the cost of a dinner as an argument and returns the cost after a .075% sales tax is added.3b. Write a function that takes the cost of a dinner and tax and adds a 20% tip to the total, then returns the total.3c. Write a function that takes a list of food names(choose them yourself) as an argument and returns the cost of purchasing all those items.3d. Write a function that takes a list of food names as an argument and returns the total cost of having a meal including tax and tip.4 . In the next cell is a 1000-digit number, write a function to solve Project Euler 8 https://projecteuler.net/problem=8 | thoudigits = 7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450
| _____no_output_____ | MIT | Python Workshop/Functions.ipynb | CalPolyPat/Python-Workshop |
LambdaNext we will look at a special type of function called a lambda. A lambda is a single line, single expression function. It is perfect for evaluating mathematical expressions like x^2 and e^sin(x^cos(x)). To write a lambda function, we use the following syntax: func = lambda : for example: xsq = lambda x:x**2 xsq(4) returns 16Lambdas will return the result of the expression. Let's check it out. | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#^^^Some junk we will learn later^^^
func = lambda x:np.exp(np.sin(x**np.cos(x)))
#^^^The important part^^^
plt.plot(np.linspace(0,10,1000), func(np.linspace(0,10,1000)))
#^^^We will learn this next^^^ | _____no_output_____ | MIT | Python Workshop/Functions.ipynb | CalPolyPat/Python-Workshop |
Exploring colour channels In this session, we'll be looking at how to explore the different colour channels that compris an image. | # We need to include the home directory in our path, so we can read in our own module.
import os
# image processing tools
import cv2
import numpy as np
# utility functions for this course
import sys
sys.path.append(os.path.join("..", "..", "CDS-VIS"))
from utils.imutils import jimshow
from utils.imutils import jimshow_channel
# plotting tool
import matplotlib.pyplot as plt | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
Rotation | filename = os.path.join("..", "..", "CDS-VIS", "img", "terasse.jpeg")
image = cv2.imread(filename)
image.shape
jimshow(image) | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
Splitting channels | (B, G, R) = cv2.split(image)
jimshow_channel(R, "Red") | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
__Empty numpy array__ | zeros = np.zeros(image.shape[:2], dtype = "uint8")
jimshow(cv2.merge([zeros, zeros, R]))
jimshow(cv2.merge([zeros, G, zeros]))
jimshow(cv2.merge([B, zeros, zeros])) | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
Histograms | jimshow_channel(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), "Greyscale") | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
__A note on ```COLOR_BRG2GRAY```__ | greyed_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
```greyed_image.flatten() != image.flatten()``` A quick greyscale histogram using matplotlib | # Create figure
plt.figure()
# Add histogram
plt.hist(image.flatten(), 256, [0,256])
# Plot title
plt.title("Greyscale histogram")
plt.xlabel("Bins")
plt.ylabel("# of Pixels")
plt.show() | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
Plotting color histograms ```cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])```- images : it is the source image of type uint8 or float32 represented as โ[img]โ.- channels : it is the index of channel for which we calculate histogram. - For grayscale image, its value is [0] and - color image, you can pass [0], [1] or [2] to calculate histogram of blue, green or red channel respectively.- mask : mask image. To find histogram of full image, it is given as โNoneโ.- histSize : this represents our BIN count. For full scale, we pass [256].- ranges : this is our RANGE. Normally, it is [0,256]. | # split channels
channels = cv2.split(image)
# names of colours
colors = ("b", "g", "r")
# create plot
plt.figure()
# add title
plt.title("Histogram")
# Add xlabel
plt.xlabel("Bins")
# Add ylabel
plt.ylabel("# of Pixels")
# for every tuple of channel, colour
for (channel, color) in zip(channels, colors):
# Create a histogram
hist = cv2.calcHist([channel], [0], None, [256], [0, 256])
# Plot histogram
plt.plot(hist, color=color)
# Set limits of x-axis
plt.xlim([0, 256])
# Show plot
plt.show() | _____no_output_____ | MIT | notebooks/session2_inclass_rdkm.ipynb | Rysias/cds-visual |
[๋ชจ๋ 2.1] SageMaker ํด๋ฌ์คํฐ์์ ํ๋ จ (No VPC์์ ์คํ)์ด ๋
ธํธ๋ถ์ ์๋์ ์์
์ ์คํ ํฉ๋๋ค.- SageMaker Hosting Cluster ์์ ํ๋ จ์ ์คํ- ํ๋ จํ Job ์ด๋ฆ์ ์ ์ฅ - ๋ค์ ๋
ธํธ๋ถ์์ ๋ชจ๋ธ ๋ฐฐํฌ ๋ฐ ์ถ๋ก ์์ ์ฌ์ฉ ํฉ๋๋ค.--- SageMaker์ ์ธ์
์ ์ป๊ณ , role ์ ๋ณด๋ฅผ ๊ฐ์ ธ์ต๋๋ค.- ์์ ๋ ์ ๋ณด๋ฅผ ํตํด์ SageMaker Hosting Cluster์ ์ฐ๊ฒฐํฉ๋๋ค. | import os
import sagemaker
from sagemaker import get_execution_role
sagemaker_session = sagemaker.Session()
role = get_execution_role() | _____no_output_____ | MIT | scratch/working/2.2.NoVPC-EFS-Train-Model.ipynb | gonsoomoon-ml/SageMaker-With-Secure-VPC |
๋ก์ปฌ์ ๋ฐ์ดํฐ S3 ์
๋ก๋ฉ๋ก์ปฌ์ ๋ฐ์ดํฐ๋ฅผ S3์ ์
๋ก๋ฉํ์ฌ ํ๋ จ์์ Input์ผ๋ก ์ฌ์ฉ ํฉ๋๋ค. | # dataset_location = sagemaker_session.upload_data(path='data', key_prefix='data/DEMO-cifar10')
# display(dataset_location)
dataset_location = 's3://sagemaker-ap-northeast-2-057716757052/data/DEMO-cifar10'
dataset_location
# efs_dir = '/home/ec2-user/efs/data'
# ! ls {efs_dir} -al
# ! aws s3 cp {dataset_location} {efs_dir} --recursive
from sagemaker.inputs import FileSystemInput
# Specify EFS ile system id.
file_system_id = 'fs-38dc1558' # 'fs-xxxxxxxx'
print(f"EFS file-system-id: {file_system_id}")
# Specify directory path for input data on the file system.
# You need to provide normalized and absolute path below.
train_file_system_directory_path = '/data/train'
eval_file_system_directory_path = '/data/eval'
validation_file_system_directory_path = '/data/validation'
print(f'EFS file-system data input path: {train_file_system_directory_path}')
print(f'EFS file-system data input path: {eval_file_system_directory_path}')
print(f'EFS file-system data input path: {validation_file_system_directory_path}')
# Specify the access mode of the mount of the directory associated with the file system.
# Directory must be mounted 'ro'(read-only).
file_system_access_mode = 'ro'
# Specify your file system type
file_system_type = 'EFS'
train = FileSystemInput(file_system_id=file_system_id,
file_system_type=file_system_type,
directory_path=train_file_system_directory_path,
file_system_access_mode=file_system_access_mode)
eval = FileSystemInput(file_system_id=file_system_id,
file_system_type=file_system_type,
directory_path=eval_file_system_directory_path,
file_system_access_mode=file_system_access_mode)
validation = FileSystemInput(file_system_id=file_system_id,
file_system_type=file_system_type,
directory_path=validation_file_system_directory_path,
file_system_access_mode=file_system_access_mode)
aws_region = 'ap-northeast-2'# aws-region-code e.g. us-east-1
s3_bucket = 'sagemaker-ap-northeast-2-057716757052'# your-s3-bucket-name
prefix = "cifar10/efs" #prefix in your bucket
s3_output_location = f's3://{s3_bucket}/{prefix}/output'
print(f'S3 model output location: {s3_output_location}')
security_group_ids = ['sg-0192524ef63ec6138'] # ['sg-xxxxxxxx']
# subnets = ['subnet-0a84bcfa36d3981e6','subnet-0304abaaefc2b1c34','subnet-0a2204b79f378b178'] # [ 'subnet-xxxxxxx', 'subnet-xxxxxxx', 'subnet-xxxxxxx']
subnets = ['subnet-0a84bcfa36d3981e6'] # [ 'subnet-xxxxxxx', 'subnet-xxxxxxx', 'subnet-xxxxxxx']
from sagemaker.tensorflow import TensorFlow
estimator = TensorFlow(base_job_name='cifar10',
entry_point='cifar10_keras_sm_tf2.py',
source_dir='training_script',
role=role,
framework_version='2.0.0',
py_version='py3',
script_mode=True,
hyperparameters={'epochs' : 1},
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
output_path=s3_output_location,
subnets=subnets,
security_group_ids=security_group_ids,
session = sagemaker.Session()
)
estimator.fit({'train': train,
'validation': validation,
'eval': eval,
})
# estimator.fit({'train': 'file://data/train',
# 'validation': 'file://data/validation',
# 'eval': 'file://data/eval'}) | train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_count has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | scratch/working/2.2.NoVPC-EFS-Train-Model.ipynb | gonsoomoon-ml/SageMaker-With-Secure-VPC |
VPC_Mode๋ฅผ True, False ์ ํ **[์ค์] VPC_Mode์์ ์คํ์์ True๋ก ๋ณ๊ฒฝํด์ฃผ์ธ์** | VPC_Mode = False
from sagemaker.tensorflow import TensorFlow
def retrieve_estimator(VPC_Mode):
if VPC_Mode:
# VPC ๋ชจ๋ ๊ฒฝ์ฐ์ subnets, security_group์ ๊ธฐ์ ํฉ๋๋ค.
estimator = TensorFlow(base_job_name='cifar10',
entry_point='cifar10_keras_sm_tf2.py',
source_dir='training_script',
role=role,
framework_version='2.0.0',
py_version='py3',
script_mode=True,
hyperparameters={'epochs': 2},
train_instance_count=1,
train_instance_type='ml.p3.8xlarge',
subnets = ['subnet-090c1fad32165b0fa','subnet-0bd7cff3909c55018'],
security_group_ids = ['sg-0f45d634d80aef27e']
)
else:
estimator = TensorFlow(base_job_name='cifar10',
entry_point='cifar10_keras_sm_tf2.py',
source_dir='training_script',
role=role,
framework_version='2.0.0',
py_version='py3',
script_mode=True,
hyperparameters={'epochs': 2},
train_instance_count=1,
train_instance_type='ml.p3.8xlarge')
return estimator
estimator = retrieve_estimator(VPC_Mode) | train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_count has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
train_instance_type has been renamed in sagemaker>=2.
See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
| MIT | scratch/working/2.2.NoVPC-EFS-Train-Model.ipynb | gonsoomoon-ml/SageMaker-With-Secure-VPC |
ํ์ต์ ์ํํฉ๋๋ค. ์ด๋ฒ์๋ ๊ฐ๊ฐ์ ์ฑ๋(`train, validation, eval`)์ S3์ ๋ฐ์ดํฐ ์ ์ฅ ์์น๋ฅผ ์ง์ ํฉ๋๋ค.ํ์ต ์๋ฃ ํ Billable seconds๋ ํ์ธํด ๋ณด์ธ์. Billable seconds๋ ์ค์ ๋ก ํ์ต ์ํ ์ ๊ณผ๊ธ๋๋ ์๊ฐ์
๋๋ค.```Billable seconds: ```์ฐธ๊ณ ๋ก, `ml.p2.xlarge` ์ธ์คํด์ค๋ก 5 epoch ํ์ต ์ ์ ์ฒด 6๋ถ-7๋ถ์ด ์์๋๊ณ , ์ค์ ํ์ต์ ์์๋๋ ์๊ฐ์ 3๋ถ-4๋ถ์ด ์์๋ฉ๋๋ค. | %%time
estimator.fit({'train':'{}/train'.format(dataset_location),
'validation':'{}/validation'.format(dataset_location),
'eval':'{}/eval'.format(dataset_location)}) | 2021-01-27 04:02:44 Starting - Starting the training job...
2021-01-27 04:03:08 Starting - Launching requested ML instancesProfilerReport-1611720164: InProgress
.........
2021-01-27 04:04:29 Starting - Preparing the instances for training......
2021-01-27 04:05:44 Downloading - Downloading input data
2021-01-27 04:05:44 Training - Downloading the training image...
2021-01-27 04:06:11 Training - Training image download completed. Training in progress..[34m2021-01-27 04:06:06,541 sagemaker-containers INFO Imported framework sagemaker_tensorflow_container.training[0m
[34m2021-01-27 04:06:07,035 sagemaker-containers INFO Invoking user script
[0m
[34mTraining Env:
[0m
[34m{
"additional_framework_parameters": {},
"channel_input_dirs": {
"eval": "/opt/ml/input/data/eval",
"validation": "/opt/ml/input/data/validation",
"train": "/opt/ml/input/data/train"
},
"current_host": "algo-1",
"framework_module": "sagemaker_tensorflow_container.training:main",
"hosts": [
"algo-1"
],
"hyperparameters": {
"model_dir": "s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model",
"epochs": 2
},
"input_config_dir": "/opt/ml/input/config",
"input_data_config": {
"eval": {
"TrainingInputMode": "File",
"S3DistributionType": "FullyReplicated",
"RecordWrapperType": "None"
},
"validation": {
"TrainingInputMode": "File",
"S3DistributionType": "FullyReplicated",
"RecordWrapperType": "None"
},
"train": {
"TrainingInputMode": "File",
"S3DistributionType": "FullyReplicated",
"RecordWrapperType": "None"
}
},
"input_dir": "/opt/ml/input",
"is_master": true,
"job_name": "cifar10-2021-01-27-04-02-44-183",
"log_level": 20,
"master_hostname": "algo-1",
"model_dir": "/opt/ml/model",
"module_dir": "s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/source/sourcedir.tar.gz",
"module_name": "cifar10_keras_sm_tf2",
"network_interface_name": "eth0",
"num_cpus": 32,
"num_gpus": 4,
"output_data_dir": "/opt/ml/output/data",
"output_dir": "/opt/ml/output",
"output_intermediate_dir": "/opt/ml/output/intermediate",
"resource_config": {
"current_host": "algo-1",
"hosts": [
"algo-1"
],
"network_interface_name": "eth0"
},
"user_entry_point": "cifar10_keras_sm_tf2.py"[0m
[34m}
[0m
[34mEnvironment variables:
[0m
[34mSM_HOSTS=["algo-1"][0m
[34mSM_NETWORK_INTERFACE_NAME=eth0[0m
[34mSM_HPS={"epochs":2,"model_dir":"s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model"}[0m
[34mSM_USER_ENTRY_POINT=cifar10_keras_sm_tf2.py[0m
[34mSM_FRAMEWORK_PARAMS={}[0m
[34mSM_RESOURCE_CONFIG={"current_host":"algo-1","hosts":["algo-1"],"network_interface_name":"eth0"}[0m
[34mSM_INPUT_DATA_CONFIG={"eval":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"},"train":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"},"validation":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"}}[0m
[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data[0m
[34mSM_CHANNELS=["eval","train","validation"][0m
[34mSM_CURRENT_HOST=algo-1[0m
[34mSM_MODULE_NAME=cifar10_keras_sm_tf2[0m
[34mSM_LOG_LEVEL=20[0m
[34mSM_FRAMEWORK_MODULE=sagemaker_tensorflow_container.training:main[0m
[34mSM_INPUT_DIR=/opt/ml/input[0m
[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config[0m
[34mSM_OUTPUT_DIR=/opt/ml/output[0m
[34mSM_NUM_CPUS=32[0m
[34mSM_NUM_GPUS=4[0m
[34mSM_MODEL_DIR=/opt/ml/model[0m
[34mSM_MODULE_DIR=s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/source/sourcedir.tar.gz[0m
[34mSM_TRAINING_ENV={"additional_framework_parameters":{},"channel_input_dirs":{"eval":"/opt/ml/input/data/eval","train":"/opt/ml/input/data/train","validation":"/opt/ml/input/data/validation"},"current_host":"algo-1","framework_module":"sagemaker_tensorflow_container.training:main","hosts":["algo-1"],"hyperparameters":{"epochs":2,"model_dir":"s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model"},"input_config_dir":"/opt/ml/input/config","input_data_config":{"eval":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"},"train":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"},"validation":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"}},"input_dir":"/opt/ml/input","is_master":true,"job_name":"cifar10-2021-01-27-04-02-44-183","log_level":20,"master_hostname":"algo-1","model_dir":"/opt/ml/model","module_dir":"s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/source/sourcedir.tar.gz","module_name":"cifar10_keras_sm_tf2","network_interface_name":"eth0","num_cpus":32,"num_gpus":4,"output_data_dir":"/opt/ml/output/data","output_dir":"/opt/ml/output","output_intermediate_dir":"/opt/ml/output/intermediate","resource_config":{"current_host":"algo-1","hosts":["algo-1"],"network_interface_name":"eth0"},"user_entry_point":"cifar10_keras_sm_tf2.py"}[0m
[34mSM_USER_ARGS=["--epochs","2","--model_dir","s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model"][0m
[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate[0m
[34mSM_CHANNEL_EVAL=/opt/ml/input/data/eval[0m
[34mSM_CHANNEL_VALIDATION=/opt/ml/input/data/validation[0m
[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train[0m
[34mSM_HP_MODEL_DIR=s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model[0m
[34mSM_HP_EPOCHS=2[0m
[34mPYTHONPATH=/opt/ml/code:/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages
[0m
[34mInvoking script with the following command:
[0m
[34m/usr/bin/python3 cifar10_keras_sm_tf2.py --epochs 2 --model_dir s3://sagemaker-ap-northeast-2-057716757052/cifar10-2021-01-27-04-02-44-183/model
[0m
[34mTrain for 312 steps, validate for 78 steps[0m
[34mEpoch 1/2[0m
[34m#015 1/312 [..............................] - ETA: 34:31 - loss: 3.5045 - accuracy: 0.1094#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 7/312 [..............................] - ETA: 4:52 - loss: 3.1433 - accuracy: 0.1462 #010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 13/312 [>.............................] - ETA: 2:35 - loss: 2.9194 - accuracy: 0.1587#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 19/312 [>.............................] - ETA: 1:45 - loss: 2.7623 - accuracy: 0.1641#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 26/312 [=>............................] - ETA: 1:15 - loss: 2.6259 - accuracy: 0.1683#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 32/312 [==>...........................] - ETA: 1:00 - loss: 2.5445 - accuracy: 0.1753#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 39/312 [==>...........................] - ETA: 48s - loss: 2.4627 - accuracy: 0.1873 #010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 45/312 [===>..........................] - ETA: 41s - loss: 2.4148 - accuracy: 0.1951#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 51/312 [===>..........................] - ETA: 36s - loss: 2.3721 - accuracy: 0.2028#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 57/312 [====>.........................] - ETA: 31s - loss: 2.3383 - accuracy: 0.2057#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 64/312 [=====>........................] - ETA: 27s - loss: 2.2982 - accuracy: 0.2120#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 71/312 [=====>........................] - ETA: 24s - loss: 2.2635 - accuracy: 0.2171#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 78/312 [======>.......................] - ETA: 21s - loss: 2.2315 - accuracy: 0.2229#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 85/312 [=======>......................] - ETA: 19s - loss: 2.2051 - accuracy: 0.2268#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 92/312 [=======>......................] - ETA: 17s - loss: 2.1798 - accuracy: 0.2320#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 99/312 [========>.....................] - ETA: 16s - loss: 2.1550 - accuracy: 0.2371#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015106/312 [=========>....................] - ETA: 14s - loss: 2.1355 - accuracy: 0.2412#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015113/312 [=========>....................] - ETA: 13s - loss: 2.1166 - accuracy: 0.2458#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015120/312 [==========>...................] - ETA: 12s - loss: 2.0997 - accuracy: 0.2493#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015127/312 [===========>..................] - ETA: 11s - loss: 2.0852 - accuracy: 0.2542#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015134/312 [===========>..................] - ETA: 10s - loss: 2.0716 - accuracy: 0.2577#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015140/312 [============>.................] - ETA: 9s - loss: 2.0586 - accuracy: 0.2616 #010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015147/312 [=============>................] - ETA: 8s - loss: 2.0466 - accuracy: 0.2645#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015154/312 [=============>................] - ETA: 8s - loss: 2.0331 - accuracy: 0.2677#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015161/312 [==============>...............] - ETA: 7s - loss: 2.0210 - accuracy: 0.2723#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015168/312 [===============>..............] - ETA: 6s - loss: 2.0082 - accuracy: 0.2766#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015175/312 [===============>..............] - ETA: 6s - loss: 1.9988 - accuracy: 0.2790#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015181/312 [================>.............] - ETA: 5s - loss: 1.9901 - accuracy: 0.2804#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015188/312 [=================>............] - ETA: 5s - loss: 1.9790 - accuracy: 0.2836#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015195/312 [=================>............] - ETA: 4s - loss: 1.9695 - accuracy: 0.2856#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015202/312 [==================>...........] - ETA: 4s - loss: 1.9605 - accuracy: 0.2881#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015209/312 [===================>..........] - ETA: 4s - loss: 1.9531 - accuracy: 0.2906#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015216/312 [===================>..........] - ETA: 3s - loss: 1.9457 - accuracy: 0.2930#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015223/312 [====================>.........] - ETA: 3s - loss: 1.9350 - accuracy: 0.2959#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015230/312 [=====================>........] - ETA: 3s - loss: 1.9290 - accuracy: 0.2975#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015237/312 [=====================>........] - ETA: 2s - loss: 1.9219 - accuracy: 0.2991#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015244/312 [======================>.......] - ETA: 2s - loss: 1.9130 - accuracy: 0.3024#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015251/312 [=======================>......] - ETA: 2s - loss: 1.9066 - accuracy: 0.3046#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015258/312 [=======================>......] - ETA: 1s - loss: 1.9006 - accuracy: 0.3065#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015264/312 [========================>.....] - ETA: 1s - loss: 1.8959 - accuracy: 0.3079#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015271/312 [=========================>....] - ETA: 1s - loss: 1.8884 - accuracy: 0.3104#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015278/312 [=========================>....] - ETA: 1s - loss: 1.8834 - accuracy: 0.3122#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015285/312 [==========================>...] - ETA: 0s - loss: 1.8764 - accuracy: 0.3148#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015292/312 [===========================>..] - ETA: 0s - loss: 1.8714 - accuracy: 0.3172#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015299/312 [===========================>..] - ETA: 0s - loss: 1.8642 - accuracy: 0.3197#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015305/312 [============================>.] - ETA: 0s - loss: 1.8589 - accuracy: 0.3213#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015312/312 [==============================] - 10s 32ms/step - loss: 1.8530 - accuracy: 0.3232 - val_loss: 2.0282 - val_accuracy: 0.3226[0m
[34mEpoch 2/2[0m
[34m#015 1/312 [..............................] - ETA: 2s - loss: 1.4358 - accuracy: 0.4531#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 8/312 [..............................] - ETA: 2s - loss: 1.5428 - accuracy: 0.4131#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 15/312 [>.............................] - ETA: 2s - loss: 1.5658 - accuracy: 0.4026#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 22/312 [=>............................] - ETA: 2s - loss: 1.5621 - accuracy: 0.4116#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 29/312 [=>............................] - ETA: 2s - loss: 1.5536 - accuracy: 0.4181#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 36/312 [==>...........................] - ETA: 2s - loss: 1.5312 - accuracy: 0.4316#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 43/312 [===>..........................] - ETA: 2s - loss: 1.5190 - accuracy: 0.4391#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 50/312 [===>..........................] - ETA: 2s - loss: 1.5194 - accuracy: 0.4364#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 56/312 [====>.........................] - ETA: 2s - loss: 1.5234 - accuracy: 0.4351#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 63/312 [=====>........................] - ETA: 1s - loss: 1.5260 - accuracy: 0.4339#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 70/312 [=====>........................] - ETA: 1s - loss: 1.5249 - accuracy: 0.4376#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 77/312 [======>.......................] - ETA: 1s - loss: 1.5162 - accuracy: 0.4421#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 84/312 [=======>......................] - ETA: 1s - loss: 1.5111 - accuracy: 0.4443#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 91/312 [=======>......................] - ETA: 1s - loss: 1.5092 - accuracy: 0.4439#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015 98/312 [========>.....................] - ETA: 1s - loss: 1.5105 - accuracy: 0.4430#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015105/312 [=========>....................] - ETA: 1s - loss: 1.5119 - accuracy: 0.4424#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015112/312 [=========>....................] - ETA: 1s - loss: 1.5089 - accuracy: 0.4440#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015119/312 [==========>...................] - ETA: 1s - loss: 1.5087 - accuracy: 0.4458#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015126/312 [===========>..................] - ETA: 1s - loss: 1.5124 - accuracy: 0.4441#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015132/312 [===========>..................] - ETA: 1s - loss: 1.5132 - accuracy: 0.4441#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015139/312 [============>.................] - ETA: 1s - loss: 1.5099 - accuracy: 0.4453#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015146/312 [=============>................] - ETA: 1s - loss: 1.5104 - accuracy: 0.4464#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015153/312 [=============>................] - ETA: 1s - loss: 1.5065 - accuracy: 0.4489#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015160/312 [==============>...............] - ETA: 1s - loss: 1.5054 - accuracy: 0.4499#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015166/312 [==============>...............] - ETA: 1s - loss: 1.5030 - accuracy: 0.4507#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015172/312 [===============>..............] - ETA: 1s - loss: 1.5006 - accuracy: 0.4514#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015179/312 [================>.............] - ETA: 1s - loss: 1.4972 - accuracy: 0.4527#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015186/312 [================>.............] - 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ETA: 0s - loss: 1.4600 - accuracy: 0.4679#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015296/312 [===========================>..] - ETA: 0s - loss: 1.4562 - accuracy: 0.4693#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015303/312 [============================>.] - ETA: 0s - loss: 1.4529 - accuracy: 0.4707#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015310/312 [============================>.] - ETA: 0s - loss: 1.4507 - accuracy: 0.4713#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#010#015312/312 [==============================] - 3s 10ms/step - loss: 1.4498 - accuracy: 0.4717 - val_loss: 1.6843 - val_accuracy: 0.4161[0m
2021-01-27 04:12:46 Uploading - Uploading generated training model[34m2021-01-27 04:12:39.226548: W tensorflow/python/util/util.cc:299] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.[0m
[34mWARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.[0m
[34mInstructions for updating:[0m
[34mIf using Keras pass *_constraint arguments to layers.[0m
[34mWARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.[0m
[34mInstructions for updating:[0m
[34mIf using Keras pass *_constraint arguments to layers.[0m
[34mINFO:tensorflow:Assets written to: /opt/ml/model/1/assets[0m
[34mINFO:tensorflow:Assets written to: /opt/ml/model/1/assets[0m
[34m2021-01-27 04:12:42,835 sagemaker-containers INFO Reporting training SUCCESS[0m
2021-01-27 04:13:16 Completed - Training job completed
ProfilerReport-1611720164: NoIssuesFound
Training seconds: 452
Billable seconds: 452
CPU times: user 1.59 s, sys: 1.44 ms, total: 1.59 s
Wall time: 10min 46s
| MIT | scratch/working/2.2.NoVPC-EFS-Train-Model.ipynb | gonsoomoon-ml/SageMaker-With-Secure-VPC |
training_job_name ์ ์ฅํ์ฌ์ training_job_name์ ์ ์ฅ ํฉ๋๋ค.- training_job_name์ ์๋ ํ๋ จ์ ๊ด๋ จ ๋ด์ฉ ๋ฐ ํ๋ จ ๊ฒฐ๊ณผ์ธ **Model Artifact** ํ์ผ์ S3 ๊ฒฝ๋ก๋ฅผ ์ ๊ณต ํฉ๋๋ค. | train_job_name = estimator._current_job_name
%store train_job_name | Stored 'train_job_name' (str)
| MIT | scratch/working/2.2.NoVPC-EFS-Train-Model.ipynb | gonsoomoon-ml/SageMaker-With-Secure-VPC |
Running scripts with python shell | #!pip install tensorflow==1.14.0
#!pip install tensorflow-base==1.14.0
#!pip install tensorflow-gpu==1.14.0
%tensorflow_version 1.x
! python main_train.py --config config_default.json | _____no_output_____ | MIT | notebooks/test_0_lstm_shell_colab.ipynb | SPRACE/track-ml |
Plot Predicted Data | import os
import json
import numpy as np
import pandas as pd
configs = json.load(open('config_default.json', 'r'))
cylindrical = configs['data']['cylindrical'] # set to polar or cartesian coordenates
normalise = configs['data']['normalise']
name = configs['model']['name']
if cylindrical:
coord = 'cylin'
else:
coord = 'xyz'
path1 = 'results/x_true_%s_%s.csv' % (name, coord)
path2 = 'results/y_true_%s_%s.csv' % (name, coord)
path3 = 'results/y_pred_%s_%s.csv' % (name, coord)
print('loading from .. %s' % path1)
print('loading from .. %s' % path2)
print('loading from .. %s' % path3)
df_test = pd.read_csv(path1, header=None)
df_true = pd.read_csv(path2, header=None)
df_pred = pd.read_csv(path3, header=None)
print('shape df_test ', df_test.shape)
print('shape df_true ', df_true.shape)
print('shape df_pred ', df_pred.shape)
# concat
#y_true = pd.concat([df_test, df_true], axis = 1, ignore_index = True)
#y_pred = pd.concat([df_test, df_pred], axis = 1, ignore_index = True)
y_true = np.concatenate([df_test, df_true], axis = 1)
y_pred = np.concatenate([df_test, df_pred], axis = 1)
y_true = pd.DataFrame(y_true)
y_pred = pd.DataFrame(y_pred)
#y_true.name = 'real'
#y_pred.name = 'pred'
y_pred.columns.name = 'pred'
y_true.columns.name = 'real'
print('size y_true ', y_true.shape)
print('size y_pred ', y_pred.shape)
from core.utils.utils import *
import warnings
N_tracks = 30
path_html = ''
name = configs['model']['name']
fig = track_plot_xyz([y_true, y_pred], n_hits = 10, cylindrical = cylindrical, n_tracks = N_tracks,
title='Track Prediction #10 Hit - Model %s (Nearest hits)' % name.upper())
fig.show()
| _____no_output_____ | MIT | notebooks/test_0_lstm_shell_colab.ipynb | SPRACE/track-ml |
Matrix Profile IntroductionThe matrix profile (MP) is a data structure and associated algorithms that helps solve the dual problem of anomaly detection and motif discovery. It is robust, scalable and largely parameter-free.MP can be combined with other algorithms to accomplish:* Motif discovery* Time series chains* Anomaly discovery* Joins* Semantic segmentationmatrixprofile-ts offers 3 different algorithms to compute Matrix Profile:* STAMP (Scalable Time Series Anytime Matrix Profile) - Each distance profile is independent of other distance profiles, the order in which they are computed can be random. It is an anytime algorithm.* STOMP (Scalable Time Series Ordered Matrix Profile) - This algorithm is an exact ordered algorithm. It is significantly faster than STAMP.* SCRIMP++ (Scalable Column Independent Matrix Profile) - This algorithm combines the anytime component of STAMP with the speed of STOMP.See: https://towardsdatascience.com/introduction-to-matrix-profiles-5568f3375d90 Code Example | !pip install matrixprofile-ts
import pandas as pd
## example data importing
data = pd.read_csv('https://raw.githubusercontent.com/iotanalytics/IoTTutorial/main/data/SCG_data.csv').drop('Unnamed: 0',1).to_numpy()[0:20,:1000]
import operator
import numpy as np
import matplotlib.pyplot as plt
from matrixprofile import *
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
# Pull a portion of the data
pattern = data[10,:] + max(abs(data[10,:]))
# Compute Matrix Profile
m = 10
mp = matrixProfile.stomp(pattern,m)
#Append np.nan to Matrix profile to enable plotting against raw data
mp_adj = np.append(mp[0],np.zeros(m-1)+np.nan)
#Plot the signal data
fig, (ax1, ax2) = plt.subplots(2,1,sharex=True,figsize=(20,10))
ax1.plot(np.arange(len(pattern)),pattern)
ax1.set_ylabel('Signal', size=22)
#Plot the Matrix Profile
ax2.plot(np.arange(len(mp_adj)),mp_adj, label="Matrix Profile", color='red')
ax2.set_ylabel('Matrix Profile', size=22)
ax2.set_xlabel('Time', size=22); | _____no_output_____ | MIT | code/preprocessing_and_decomposition/Matrix_Profile.ipynb | iotanalytics/IoTTutorial |
Steps to build a Neural Network1. Empty Model (sequential/Model)2 | import tensorflow.keras.datasets as kd
data = kd.fashion_mnist.load_data()
(xtrain,ytrain),(xtest,ytest) = data
xtrain.shape
import matplotlib.pyplot as plt
plt.imshow(xtrain[0,:,:],cmap='gray_r')
ytrain[0]
xtrain1 = xtrain.reshape(-1,28*28)
xtest1 = xtest.reshape(-1,28*28)
xtrain1.shape
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model_ann = Sequential()
model_ann.add(Dense(units=128, input_shape=(784,), activation='relu'))
model_ann.add(Dense(units=128, activation='relu'))
model_ann.add(Dense(units=10, activation='softmax'))
model_ann.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model_ann.summary()
1st layer =
history = model_ann.fit(xtrain1,ytrain,epochs=10)
plt.plot(history.history['loss'])
plt.plot(history.history['accuracy'])
plt.grid()
plt.
plt.xticks(range(1,11))
plt.xlabel('Epochs-->')
plt.show()
ypred = model_ann.predict(xtest1)
labels.get(ytest[0])
ypred[0].argmax()
model_ann.evaluate(xtest1,ytest) | 313/313 [==============================] - 1s 2ms/step - loss: 0.4793 - accuracy: 0.8335
| MIT | day37_ML_ANN_RNN.ipynb | DynamicEngine2001/Programming-Codes |
Churn Modelling | import pandas as pd
df = pd.read_csv('Churn_Modelling.csv')
df
df.info()
df1 = pd.get_dummies(df)
df1.head() | _____no_output_____ | MIT | day37_ML_ANN_RNN.ipynb | DynamicEngine2001/Programming-Codes |
Recurrent Neural Network | import numpy as np
stock_data = pd.read_csv('stock_data.csv')
fb = stock_data[['Open']] [stock_data['Stock']=='FB'].copy()
fb.head()
fb = fb.values
fb.shape
x = []
y = []
for i in range(20, len(fb)):
x.append(fb['Open'].valuesfb[i-20:1].tolist())
y.append(fb[i].tolist())
| _____no_output_____ | MIT | day37_ML_ANN_RNN.ipynb | DynamicEngine2001/Programming-Codes |
Python Modules | %%writefile weather.py
def prognosis():
print("It will rain today")
import weather
weather.prognosis() | It will rain today
| MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
How does Python know from where to import packages/modules from? | # Python imports work by searching the directories listed in sys.path.
import sys
sys.path
## "__main__" usage
# A module can discover whether or not it is running in the main scope by checking its own __name__,
# which allows a common idiom for conditionally executing code in a module when it is run as a script or with python -m
# but not when it is imported:
%%writefile hw.py
#!/usr/bin/env python
def hw():
print("Running Main")
def hw2():
print("Hello 2")
if __name__ == "__main__":
# execute only if run as a script
print("Running as script")
hw()
hw2()
import main
import hw
main.main()
hw.hw2()
# Running on all 3 OSes from command line:
python main.py | _____no_output_____ | MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
Make main.py self running on Linux (also should work on MacOS): Add !/usr/bin/env python to first line of scriptmark it executable using need to change permissions too!$ chmod +x main.py Making Standalone .EXEs for Python in Windows * http://www.py2exe.org/ used to be for Python 2 , now supposedly Python 3 as well* http://www.pyinstaller.org/ Tutorial: https://medium.com/dreamcatcher-its-blog/making-an-stand-alone-executable-from-a-python-script-using-pyinstaller-d1df9170e263 Need to create exe on a similar system as target system! | # Exercise Write a function which returns a list of fibonacci numbers up to starting with 1, 1, 2, 3, 5 up to the nth.
So Fib(4) would return [1,1,2,3] | _____no_output_____ | MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
  | %%writefile fibo.py
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 1 1
while b < n:
print(b, end=' ')
a, b = b, a+b
print()
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 1, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
import fibo
fibo.fib(100)
fibo.fib2(100)
fib=fibo.fib | _____no_output_____ | MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
If you intend to use a function often you can assign it to a local name: | fib(300) | 1 1 2 3 5 8 13 21 34 55 89 144 233
| MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
There is a variant of the import statement that imports names from a module directly into the importing moduleโs symbol table. | from fibo import fib, fib2 # we overwrote fib=fibo.fib
fib(100)
fib2(200) | _____no_output_____ | MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
This does not introduce the module name from which the imports are taken in the local symbol table (so in the example, fibo is not defined). There is even a variant to import all names that a module defines: **NOT RECOMMENDED** | ## DO not do this Namespace collission possible!!
from fibo import *
fib(400) | 1 1 2 3 5 8 13 21 34 55 89 144 233 377
| MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
If the module name is followed by as, then the name following as is bound directly to the imported module. | import fibo as fib
dir(fib)
fib.fib(50)
### It can also be used when utilising from with similar effects:
from fibo import fib as fibonacci
fibonacci(200) | 1 1 2 3 5 8 13 21 34 55 89 144
| MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
Executing modules as scriptsยถ When you run a Python module withpython fibo.py the code in the module will be executed, just as if you imported it, but with the \_\_name\_\_ set to "\_\_main\_\_". That means that by adding this code at the end of your module: | %%writefile fibbo.py
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print(b, end=' ')
a, b = b, a+b
print()
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
if __name__ == "__main__":
import sys
fib(int(sys.argv[1], 10))
import fibbo as fi
fi.fib(200) | 1 1 2 3 5 8 13 21 34 55 89 144
| MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite). The Module Search PathWhen a module named spam is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named spam.py in a list of directories given by the variable sys.path. sys.path is initialized from these locations:* The directory containing the input script (or the current directory when no file is specified).* PYTHONPATH (a list of directory names, with the same syntax as the shell variable PATH).* The installation-dependent default. Packages are a way of structuring Pythonโs module namespace by using โdotted module namesโ. For example, the module name A.B designates a submodule named B in a package named A. Just like the use of modules saves the authors of different modules from having to worry about each otherโs global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or Pillow from having to worry about each otherโs module names. | sound/ Top-level package
__init__.py Initialize the sound package
formats/ Subpackage for file format conversions
__init__.py
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
__init__.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
__init__.py
equalizer.py
vocoder.py
karaoke.py
... | _____no_output_____ | MIT | Python_Core/Python Modules and Imports.ipynb | ValRCS/RCS_Python_11 |
Quick analysis | from phimal_utilities.analysis import Results
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='notebook', style='white')
%config InlineBackend.figure_format = 'svg'
data_mt = Results('runs/testing_multitask_unnormalized//')
data_bl = Results('runs/testing_normal_unnormalized//')
keys = data_mt.keys
fig, axes = plt.subplots(figsize=(10, 3), constrained_layout=True, ncols=2)
ax = axes[0]
ax.semilogy(data_bl.df.index, data_bl.df[keys['mse']], label='Baseline')
ax.semilogy(data_mt.df.index, data_mt.df[keys['mse']], label='Multitask')
ax.set_title('MSE')
ax.set_xlabel('Epoch', weight='bold')
ax.set_ylabel('Cost', weight='bold')
ax.legend()
#ax.set_xlim([0, 8000])
ax = axes[1]
ax.semilogy(data_bl.df.index, data_bl.df[keys['reg']], label='Baseline')
ax.semilogy(data_mt.df.index, data_mt.df[keys['reg']], label='Multitask')
ax.set_title('Regression')
ax.set_xlabel('Epoch', weight='bold')
ax.set_ylabel('Cost', weight='bold')
ax.legend()
#ax.set_xlim([0, 8000])
fig.show()
fig, axes = plt.subplots(ncols=3, constrained_layout=True, figsize=(15, 4))
ax = axes[0]
ax.plot(data_bl.df.index, data_bl.df[keys['coeffs']])
ax.plot(data_bl.df.index, data_bl.df[keys['coeffs'][2]], lw=3)
ax.plot(data_bl.df.index, data_bl.df[keys['coeffs'][5]], lw=3)
ax.set_ylim([-2, 2])
ax.set_title('Coefficients baseline')
ax.set_xlabel('Epoch', weight='bold')
ax.set_ylabel('Cost', weight='bold')
#ax.set_xlim([0, 8000])
ax = axes[1]
ax.plot(data_mt.df.index, data_mt.df[keys['coeffs']])
ax.plot(data_mt.df.index, data_mt.df[keys['coeffs'][2]], lw=3)
ax.plot(data_mt.df.index, data_mt.df[keys['coeffs'][5]], lw=3)
ax.set_ylim([-2, 2])
ax.set_title('Coefficients Multitask')
ax.set_xlabel('Epoch', weight='bold')
ax.set_ylabel('Cost', weight='bold')
#ax.set_xlim([0, 8000])
ax = axes[2]
true_coeffs = np.zeros(len(keys['unscaled_coeffs']))
true_coeffs[2] = 0.1
true_coeffs[5] = -1
ax.semilogy(data_bl.df.index, np.mean(np.abs(data_bl.df[keys['unscaled_coeffs']] - true_coeffs), axis=1), label='Baseline')
ax.semilogy(data_mt.df.index, np.mean(np.abs(data_mt.df[keys['unscaled_coeffs']] - true_coeffs), axis=1), label='Baseline')
ax.set_ylim([-5, 2])
ax.legend()
fig.show() | _____no_output_____ | MIT | notebooks/testing_multitask.ipynb | GJBoth/MultiTaskPINN |
What is `torch.nn` *really*?============================by Jeremy Howard, `fast.ai `_. Thanks to Rachel Thomas and Francisco Ingham. We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file,click `here `_ .PyTorch provides the elegantly designed modules and classes `torch.nn `_ ,`torch.optim `_ ,`Dataset `_ ,and `DataLoader `_to help you create and train neural networks.In order to fully utilize their power and customizethem for your problem, you need to really understand exactly what they'redoing. To develop this understanding, we will first train basic neural neton the MNIST data set without using any features from these models; we willinitially only use the most basic PyTorch tensor functionality. Then, we willincrementally add one feature from ``torch.nn``, ``torch.optim``, ``Dataset``, or``DataLoader`` at a time, showing exactly what each piece does, and how itworks to make the code either more concise, or more flexible.**This tutorial assumes you already have PyTorch installed, and are familiarwith the basics of tensor operations.** (If you're familiar with Numpy arrayoperations, you'll find the PyTorch tensor operations used here nearly identical).MNIST data setup----------------We will use the classic `MNIST `_ dataset,which consists of black-and-white images of hand-drawn digits (between 0 and 9).We will use `pathlib `_for dealing with paths (part of the Python 3 standard library), and willdownload the dataset using`requests `_. We will onlyimport modules when we use them, so you can see exactly what's beingused at each point. | from pathlib import Path
import requests
DATA_PATH = Path("data")
PATH = DATA_PATH / "mnist"
PATH.mkdir(parents=True, exist_ok=True)
URL = "http://deeplearning.net/data/mnist/"
FILENAME = "mnist.pkl.gz"
if not (PATH / FILENAME).exists():
content = requests.get(URL + FILENAME).content
(PATH / FILENAME).open("wb").write(content) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
This dataset is in numpy array format, and has been stored using pickle,a python-specific format for serializing data. | import pickle
import gzip
with gzip.open((PATH / FILENAME).as_posix(), "rb") as f:
((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1") | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Each image is 28 x 28, and is being stored as a flattened row of length784 (=28x28). Let's take a look at one; we need to reshape it to 2dfirst. | from matplotlib import pyplot
import numpy as np
pyplot.imshow(x_train[0].reshape((28, 28)), cmap="gray")
print(x_train.shape) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
PyTorch uses ``torch.tensor``, rather than numpy arrays, so we need toconvert our data. | import torch
x_train, y_train, x_valid, y_valid = map(
torch.tensor, (x_train, y_train, x_valid, y_valid)
)
n, c = x_train.shape
x_train, x_train.shape, y_train.min(), y_train.max()
print(x_train, y_train)
print(x_train.shape)
print(y_train.min(), y_train.max()) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Neural net from scratch (no torch.nn)---------------------------------------------Let's first create a model using nothing but PyTorch tensor operations. We're assumingyou're already familiar with the basics of neural networks. (If you're not, you canlearn them at `course.fast.ai `_).PyTorch provides methods to create random or zero-filled tensors, which we willuse to create our weights and bias for a simple linear model. These are just regulartensors, with one very special addition: we tell PyTorch that they require agradient. This causes PyTorch to record all of the operations done on the tensor,so that it can calculate the gradient during back-propagation *automatically*!For the weights, we set ``requires_grad`` **after** the initialization, since wedon't want that step included in the gradient. (Note that a trailling ``_`` inPyTorch signifies that the operation is performed in-place.)NoteWe are initializing the weights here with `Xavier initialisation `_ (by multiplying with 1/sqrt(n)). | import math
weights = torch.randn(784, 10) / math.sqrt(784)
weights.requires_grad_()
bias = torch.zeros(10, requires_grad=True) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Thanks to PyTorch's ability to calculate gradients automatically, we canuse any standard Python function (or callable object) as a model! Solet's just write a plain matrix multiplication and broadcasted additionto create a simple linear model. We also need an activation function, sowe'll write `log_softmax` and use it. Remember: although PyTorchprovides lots of pre-written loss functions, activation functions, andso forth, you can easily write your own using plain python. PyTorch willeven create fast GPU or vectorized CPU code for your functionautomatically. | def log_softmax(x):
return x - x.exp().sum(-1).log().unsqueeze(-1)
def model(xb):
return log_softmax(xb @ weights + bias) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
In the above, the ``@`` stands for the dot product operation. We will callour function on one batch of data (in this case, 64 images). This isone *forward pass*. Note that our predictions won't be any better thanrandom at this stage, since we start with random weights. | bs = 64 # batch size
xb = x_train[0:bs] # a mini-batch from x
preds = model(xb) # predictions
preds[0], preds.shape
print(preds[0], preds.shape) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
As you see, the ``preds`` tensor contains not only the tensor values, but also agradient function. We'll use this later to do backprop.Let's implement negative log-likelihood to use as the loss function(again, we can just use standard Python): | def nll(input, target):
return -input[range(target.shape[0]), target].mean()
loss_func = nll | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's check our loss with our random model, so we can see if we improveafter a backprop pass later. | yb = y_train[0:bs]
print(loss_func(preds, yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's also implement a function to calculate the accuracy of our model.For each prediction, if the index with the largest value matches thetarget value, then the prediction was correct. | def accuracy(out, yb):
preds = torch.argmax(out, dim=1)
return (preds == yb).float().mean() | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's check the accuracy of our random model, so we can see if ouraccuracy improves as our loss improves. | print(accuracy(preds, yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
We can now run a training loop. For each iteration, we will:- select a mini-batch of data (of size ``bs``)- use the model to make predictions- calculate the loss- ``loss.backward()`` updates the gradients of the model, in this case, ``weights`` and ``bias``.We now use these gradients to update the weights and bias. We do thiswithin the ``torch.no_grad()`` context manager, because we do not want theseactions to be recorded for our next calculation of the gradient. You can readmore about how PyTorch's Autograd records operations`here `_.We then set thegradients to zero, so that we are ready for the next loop.Otherwise, our gradients would record a running tally of all the operationsthat had happened (i.e. ``loss.backward()`` *adds* the gradients to whatever isalready stored, rather than replacing them)... tip:: You can use the standard python debugger to step through PyTorch code, allowing you to check the various variable values at each step. Uncomment ``set_trace()`` below to try it out. | from IPython.core.debugger import set_trace
lr = 0.5 # learning rate
epochs = 2 # how many epochs to train for
for epoch in range(epochs):
for i in range((n - 1) // bs + 1):
# set_trace()
start_i = i * bs
end_i = start_i + bs
xb = x_train[start_i:end_i]
yb = y_train[start_i:end_i]
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
with torch.no_grad():
weights -= weights.grad * lr
bias -= bias.grad * lr
weights.grad.zero_()
bias.grad.zero_() | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
That's it: we've created and trained a minimal neural network (in this case, alogistic regression, since we have no hidden layers) entirely from scratch!Let's check the loss and accuracy and compare those to what we gotearlier. We expect that the loss will have decreased and accuracy tohave increased, and they have. | print(loss_func(model(xb), yb), accuracy(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Using torch.nn.functional------------------------------We will now refactor our code, so that it does the same thing as before, onlywe'll start taking advantage of PyTorch's ``nn`` classes to make it more conciseand flexible. At each step from here, we should be making our code one or moreof: shorter, more understandable, and/or more flexible.The first and easiest step is to make our code shorter by replacing ourhand-written activation and loss functions with those from ``torch.nn.functional``(which is generally imported into the namespace ``F`` by convention). This modulecontains all the functions in the ``torch.nn`` library (whereas other parts of thelibrary contain classes). As well as a wide range of loss and activationfunctions, you'll also find here some convenient functions for creating neuralnets, such as pooling functions. (There are also functions for doing convolutions,linear layers, etc, but as we'll see, these are usually better handled usingother parts of the library.)If you're using negative log likelihood loss and log softmax activation,then Pytorch provides a single function ``F.cross_entropy`` that combinesthe two. So we can even remove the activation function from our model. | import torch.nn.functional as F
loss_func = F.cross_entropy
def model(xb):
return xb @ weights + bias | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Note that we no longer call ``log_softmax`` in the ``model`` function. Let'sconfirm that our loss and accuracy are the same as before: | print(loss_func(model(xb), yb), accuracy(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Refactor using nn.Module-----------------------------Next up, we'll use ``nn.Module`` and ``nn.Parameter``, for a clearer and moreconcise training loop. We subclass ``nn.Module`` (which itself is a class andable to keep track of state). In this case, we want to create a class thatholds our weights, bias, and method for the forward step. ``nn.Module`` has anumber of attributes and methods (such as ``.parameters()`` and ``.zero_grad()``)which we will be using.Note``nn.Module`` (uppercase M) is a PyTorch specific concept, and is a class we'll be using a lot. ``nn.Module`` is not to be confused with the Python concept of a (lowercase ``m``) `module `_, which is a file of Python code that can be imported. | from torch import nn
class Mnist_Logistic(nn.Module):
def __init__(self):
super().__init__()
self.weights = nn.Parameter(torch.randn(784, 10) / math.sqrt(784))
self.bias = nn.Parameter(torch.zeros(10))
def forward(self, xb):
return xb @ self.weights + self.bias | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Since we're now using an object instead of just using a function, wefirst have to instantiate our model: | model = Mnist_Logistic() | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Now we can calculate the loss in the same way as before. Note that``nn.Module`` objects are used as if they are functions (i.e they are*callable*), but behind the scenes Pytorch will call our ``forward``method automatically. | print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Previously for our training loop we had to update the values for each parameterby name, and manually zero out the grads for each parameter separately, like this::: with torch.no_grad(): weights -= weights.grad * lr bias -= bias.grad * lr weights.grad.zero_() bias.grad.zero_()Now we can take advantage of model.parameters() and model.zero_grad() (whichare both defined by PyTorch for ``nn.Module``) to make those steps more conciseand less prone to the error of forgetting some of our parameters, particularlyif we had a more complicated model::: with torch.no_grad(): for p in model.parameters(): p -= p.grad * lr model.zero_grad()We'll wrap our little training loop in a ``fit`` function so we can run itagain later. | def fit():
for epoch in range(epochs):
for i in range((n - 1) // bs + 1):
start_i = i * bs
end_i = start_i + bs
xb = x_train[start_i:end_i]
yb = y_train[start_i:end_i]
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
with torch.no_grad():
for p in model.parameters():
p -= p.grad * lr
model.zero_grad()
fit() | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's double-check that our loss has gone down: | print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Refactor using nn.Linear-------------------------We continue to refactor our code. Instead of manually defining andinitializing ``self.weights`` and ``self.bias``, and calculating ``xb @self.weights + self.bias``, we will instead use the Pytorch class`nn.Linear `_ for alinear layer, which does all that for us. Pytorch has many types ofpredefined layers that can greatly simplify our code, and often makes itfaster too. | class Mnist_Logistic(nn.Module):
def __init__(self):
super().__init__()
self.lin = nn.Linear(784, 10)
def forward(self, xb):
return self.lin(xb) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
We instantiate our model and calculate the loss in the same way as before: | model = Mnist_Logistic()
print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
We are still able to use our same ``fit`` method as before. | fit()
print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Refactor using optim------------------------------Pytorch also has a package with various optimization algorithms, ``torch.optim``.We can use the ``step`` method from our optimizer to take a forward step, insteadof manually updating each parameter.This will let us replace our previous manually coded optimization step::: with torch.no_grad(): for p in model.parameters(): p -= p.grad * lr model.zero_grad()and instead use just::: opt.step() opt.zero_grad()(``optim.zero_grad()`` resets the gradient to 0 and we need to call it beforecomputing the gradient for the next minibatch.) | from torch import optim | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
We'll define a little function to create our model and optimizer so wecan reuse it in the future. | def get_model():
model = Mnist_Logistic()
return model, optim.SGD(model.parameters(), lr=lr)
model, opt = get_model()
print(loss_func(model(xb), yb))
for epoch in range(epochs):
for i in range((n - 1) // bs + 1):
start_i = i * bs
end_i = start_i + bs
xb = x_train[start_i:end_i]
yb = y_train[start_i:end_i]
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
opt.step()
opt.zero_grad()
print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Refactor using Dataset------------------------------PyTorch has an abstract Dataset class. A Dataset can be anything that hasa ``__len__`` function (called by Python's standard ``len`` function) anda ``__getitem__`` function as a way of indexing into it.`This tutorial `_walks through a nice example of creating a custom ``FacialLandmarkDataset`` classas a subclass of ``Dataset``.PyTorch's `TensorDataset `_is a Dataset wrapping tensors. By defining a length and way of indexing,this also gives us a way to iterate, index, and slice along the firstdimension of a tensor. This will make it easier to access both theindependent and dependent variables in the same line as we train. | from torch.utils.data import TensorDataset | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Both ``x_train`` and ``y_train`` can be combined in a single ``TensorDataset``,which will be easier to iterate over and slice. | train_ds = TensorDataset(x_train, y_train) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Previously, we had to iterate through minibatches of x and y values separately::: xb = x_train[start_i:end_i] yb = y_train[start_i:end_i]Now, we can do these two steps together::: xb,yb = train_ds[i*bs : i*bs+bs] | model, opt = get_model()
for epoch in range(epochs):
for i in range((n - 1) // bs + 1):
xb, yb = train_ds[i * bs: i * bs + bs]
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
opt.step()
opt.zero_grad()
print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Refactor using DataLoader------------------------------Pytorch's ``DataLoader`` is responsible for managing batches. You cancreate a ``DataLoader`` from any ``Dataset``. ``DataLoader`` makes it easierto iterate over batches. Rather than having to use ``train_ds[i*bs : i*bs+bs]``,the DataLoader gives us each minibatch automatically. | from torch.utils.data import DataLoader
train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Previously, our loop iterated over batches (xb, yb) like this::: for i in range((n-1)//bs + 1): xb,yb = train_ds[i*bs : i*bs+bs] pred = model(xb)Now, our loop is much cleaner, as (xb, yb) are loaded automatically from the data loader::: for xb,yb in train_dl: pred = model(xb) | model, opt = get_model()
for epoch in range(epochs):
for xb, yb in train_dl:
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
opt.step()
opt.zero_grad()
print(loss_func(model(xb), yb)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Thanks to Pytorch's ``nn.Module``, ``nn.Parameter``, ``Dataset``, and ``DataLoader``,our training loop is now dramatically smaller and easier to understand. Let'snow try to add the basic features necessary to create effecive models in practice.Add validation-----------------------In section 1, we were just trying to get a reasonable training loop set up foruse on our training data. In reality, you **always** should also havea `validation set `_, in orderto identify if you are overfitting.Shuffling the training data is`important `_to prevent correlation between batches and overfitting. On the other hand, thevalidation loss will be identical whether we shuffle the validation set or not.Since shuffling takes extra time, it makes no sense to shuffle the validation data.We'll use a batch size for the validation set that is twice as large asthat for the training set. This is because the validation set does notneed backpropagation and thus takes less memory (it doesn't need tostore the gradients). We take advantage of this to use a larger batchsize and compute the loss more quickly. | train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)
valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs * 2) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
We will calculate and print the validation loss at the end of each epoch.(Note that we always call ``model.train()`` before training, and ``model.eval()``before inference, because these are used by layers such as ``nn.BatchNorm2d``and ``nn.Dropout`` to ensure appropriate behaviour for these different phases.) | model, opt = get_model()
for epoch in range(epochs):
model.train()
for xb, yb in train_dl:
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
opt.step()
opt.zero_grad()
model.eval()
with torch.no_grad():
valid_loss = sum(loss_func(model(xb), yb) for xb, yb in valid_dl)
print(epoch, valid_loss / len(valid_dl)) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Create fit() and get_data()----------------------------------We'll now do a little refactoring of our own. Since we go through a similarprocess twice of calculating the loss for both the training set and thevalidation set, let's make that into its own function, ``loss_batch``, whichcomputes the loss for one batch.We pass an optimizer in for the training set, and use it to performbackprop. For the validation set, we don't pass an optimizer, so themethod doesn't perform backprop. | def loss_batch(model, loss_func, xb, yb, opt=None):
loss = loss_func(model(xb), yb)
if opt is not None:
loss.backward()
opt.step()
opt.zero_grad()
return loss.item(), len(xb) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
``fit`` runs the necessary operations to train our model and compute thetraining and validation losses for each epoch. | import numpy as np
def fit(epochs, model, loss_func, opt, train_dl, valid_dl):
for epoch in range(epochs):
model.train()
for xb, yb in train_dl:
loss_batch(model, loss_func, xb, yb, opt)
model.eval()
with torch.no_grad():
losses, nums = zip(
*[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]
)
val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
print(epoch, val_loss) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
``get_data`` returns dataloaders for the training and validation sets. | def get_data(train_ds, valid_ds, bs):
return (
DataLoader(train_ds, batch_size=bs, shuffle=True),
DataLoader(valid_ds, batch_size=bs * 2),
) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Now, our whole process of obtaining the data loaders and fitting themodel can be run in 3 lines of code: | train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
model, opt = get_model()
fit(epochs, model, loss_func, opt, train_dl, valid_dl) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
You can use these basic 3 lines of code to train a wide variety of models.Let's see if we can use them to train a convolutional neural network (CNN)!Switch to CNN-------------We are now going to build our neural network with three convolutional layers.Because none of the functions in the previous section assume anything aboutthe model form, we'll be able to use them to train a CNN without any modification.We will use Pytorch's predefined`Conv2d `_ classas our convolutional layer. We define a CNN with 3 convolutional layers.Each convolution is followed by a ReLU. At the end, we perform anaverage pooling. (Note that ``view`` is PyTorch's version of numpy's``reshape``) | class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1)
def forward(self, xb):
xb = xb.view(-1, 1, 28, 28)
xb = F.relu(self.conv1(xb))
xb = F.relu(self.conv2(xb))
xb = F.relu(self.conv3(xb))
xb = F.avg_pool2d(xb, 4)
return xb.view(-1, xb.size(1))
lr = 0.1 | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
`Momentum `_ is a variation onstochastic gradient descent that takes previous updates into account as welland generally leads to faster training. | model = Mnist_CNN()
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
fit(epochs, model, loss_func, opt, train_dl, valid_dl) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
nn.Sequential------------------------``torch.nn`` has another handy class we can use to simply our code:`Sequential `_ .A ``Sequential`` object runs each of the modules contained within it, in asequential manner. This is a simpler way of writing our neural network.To take advantage of this, we need to be able to easily define a**custom layer** from a given function. For instance, PyTorch doesn'thave a `view` layer, and we need to create one for our network. ``Lambda``will create a layer that we can then use when defining a network with``Sequential``. | class Lambda(nn.Module):
def __init__(self, func):
super().__init__()
self.func = func
def forward(self, x):
return self.func(x)
def preprocess(x):
return x.view(-1, 1, 28, 28) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
The model created with ``Sequential`` is simply: | model = nn.Sequential(
Lambda(preprocess),
nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.AvgPool2d(4),
Lambda(lambda x: x.view(x.size(0), -1)),
)
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
fit(epochs, model, loss_func, opt, train_dl, valid_dl) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Wrapping DataLoader-----------------------------Our CNN is fairly concise, but it only works with MNIST, because: - It assumes the input is a 28\*28 long vector - It assumes that the final CNN grid size is 4\*4 (since that's the averagepooling kernel size we used)Let's get rid of these two assumptions, so our model works with any 2dsingle channel image. First, we can remove the initial Lambda layer butmoving the data preprocessing into a generator: | def preprocess(x, y):
return x.view(-1, 1, 28, 28), y
class WrappedDataLoader:
def __init__(self, dl, func):
self.dl = dl
self.func = func
def __len__(self):
return len(self.dl)
def __iter__(self):
batches = iter(self.dl)
for b in batches:
yield (self.func(*b))
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
train_dl = WrappedDataLoader(train_dl, preprocess)
valid_dl = WrappedDataLoader(valid_dl, preprocess) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Next, we can replace ``nn.AvgPool2d`` with ``nn.AdaptiveAvgPool2d``, whichallows us to define the size of the *output* tensor we want, rather thanthe *input* tensor we have. As a result, our model will work with anysize input. | model = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
Lambda(lambda x: x.view(x.size(0), -1)),
)
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's try it out: | fit(epochs, model, loss_func, opt, train_dl, valid_dl) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Using your GPU---------------If you're lucky enough to have access to a CUDA-capable GPU (you canrent one for about $0.50/hour from most cloud providers) you canuse it to speed up your code. First check that your GPU is working inPytorch: | print(torch.cuda.is_available()) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
And then create a device object for it: | dev = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu") | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Let's update ``preprocess`` to move batches to the GPU: | def preprocess(x, y):
return x.view(-1, 1, 28, 28).to(dev), y.to(dev)
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
train_dl = WrappedDataLoader(train_dl, preprocess)
valid_dl = WrappedDataLoader(valid_dl, preprocess) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
Finally, we can move our model to the GPU. | model.to(dev)
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
You should find it runs faster now: | fit(epochs, model, loss_func, opt, train_dl, valid_dl) | _____no_output_____ | MIT | notebook/pytorch/nn_tutorial.ipynb | mengwangk/myinvestor-toolkit |
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