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 |
|---|---|---|---|---|---|
The maximum value rises and falls smoothly, while the minimum seems to be a step function. Neither trend seems particularly likely, so either there's a mistake in our calculations or something is wrong with our data. This insight would have been difficult to reach by examining the numbers themselves without visualisati... | min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0)) | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Grouping plotsYou can group similar plots in a single figure using subplots. This script below uses a number of new commands. The function `matplotlib.pyplot.figure()` creates a space into which we will place all of our plots. The parameter `figsize` tells Python how big to make this space. Each subplot is placed into... | import numpy
import matplotlib.pyplot
data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')
fig = matplotlib.pyplot.figure(figsize=(15.0, 5.0))
axes1 = fig.add_subplot(1, 3, 1) #(1,3,1 1つめのグラフ)
axes2 = fig.add_subplot(1, 3, 2)
axes3 = fig.add_subplot(1, 3, 3)
#label の設定
axes1.set_ylabel('average')
... | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
The Previous Plots as SubplotsThe call to `loadtxt` reads our data, and the rest of the program tells the plotting library how large we want the figure to be, that we're creating three subplots, what to draw for each one, and that we want a tight layout. (If we leave out that call to `fig.tight_layout()`, the graphs w... | import numpy
import matplotlib.pyplot
data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')
fig = matplotlib.pyplot.figure(figsize=(5.0, 5.0))
axes1 = fig.add_subplot(1, 1, 1) #(1,3,1 1つめのグラフ)
#label の設定 + #Days の設定
axes1.set_ylabel('average')
axes1.set_xlabel('days')
plot = axes1.plot(numpy.mea... | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Scientists Dislike Typing. We will always use the syntax `import numpy` to import NumPy. However, in order to save typing, it is often suggested to make a shortcut like so: `import numpy as np`. If you ever see Python code online using a NumPy function with np (for example, `np.loadtxt(...))`, it's because they've use... | import numpy as np
np.random.rand()
# | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Exercises Plot ScalingWhy do all of our plots stop just short of the upper end of our graph? Solution: If we want to change this, we can use the `set_ylim(min, max)` method of each ‘axes’, for example:```axes3.set_ylim(0,6)```Update your plotting code to automatically set a more appropriate scale. (Hint: you can make... | #see above | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Drawing Straight LinesIn the centre and right subplots above, we expect all lines to look like step functions because non-integer value are not realistic for the minimum and maximum values. However, you can see that the lines are not always vertical or horizontal, and in particular the step function in the subplot on ... | import numpy
import matplotlib.pyplot
data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')
fig = matplotlib.pyplot.figure(figsize=(15.0, 5.0))
axes1 = fig.add_subplot(1, 3, 1) #(1,3,1 1つめのグラフ)
axes2 = fig.add_subplot(1, 3, 2)
axes3 = fig.add_subplot(1, 3, 3)
#label の設定
axes1.set_ylabel('average')
... | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Solution: Make Your Own PlotCreate a plot showing the standard deviation (using `numpy.std`) of the inflammation data for each day across all patients. | #standard deviation
#help(numpy.std)
# Example
#a = np.array([[1, 2], [3, 4]])
#>>> np.std(a)
# 1.1180339887498949 # may vary
# >>> np.std(a, axis=0)
# array([1., 1.])
#>>> np.std(a, axis=1)
#array([0.5, 0.5])
numpy.std(data)
numpy.std(data, axis=0)
numpy.std(data, axis=1) | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Moving Plots AroundModify the program to display the three plots vertically rather than side by side. | import numpy
import matplotlib.pyplot
data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')
fig = matplotlib.pyplot.figure(figsize=(15.0, 15.0))
axes1 = fig.add_subplot(3, 3, 1) #(total no. of row,total no. of column, order starting from top left 1つめのグラフ)
axes2 = fig.add_subplot(3, 3, 5)
axes3 = fig... | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Stacking ArraysArrays can be concatenated and stacked on top of one another, using NumPy’s `vstack` and `hstack` functions for vertical and horizontal stacking, respectively.Run the following code to view `A`, `B` and `C` | import numpy
A = numpy.array([[1,2,3], [4,5,6], [7, 8, 9]])
print('A = ')
print(A)
B = numpy.hstack([A, A])
print('B = ')
print(B)
C = numpy.vstack([A, A])
print('C = ')
print(C) | A =
[[1 2 3]
[4 5 6]
[7 8 9]]
B =
[[1 2 3 1 2 3]
[4 5 6 4 5 6]
[7 8 9 7 8 9]]
C =
[[1 2 3]
[4 5 6]
[7 8 9]
[1 2 3]
[4 5 6]
[7 8 9]]
| MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Write some additional code that slices the first and last columns of `A`,and stacks them into a 3x2 array. Make sure to print the results to verify your solution. | print(A[:,0]) # all rows from first column
#print(result) | [1 4 7]
| MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Change In InflammationThis patient data is longitudinal in the sense that each row represents a series of observations relating to one individual. This means that the change in inflammation over time is a meaningful concept.The `numpy.diff()` function takes a NumPy array and returns the differences between two success... | npdiff = numpy.array([ 0, 2, 5, 9, 14])
numpy.diff(npdiff) | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
In our `data` Which axis would it make sense to use this function along? | npdiff = numpy.array(data[0][0:4])
print(npdiff) | [0. 0. 1. 3.]
| MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Solution If the shape of an individual data file is (60, 40) (60 rows and 40 columns), what would the shape of the array be after you run the diff() function and why? | npdiff = numpy.array(data[0:60][0:39])
print(npdiff) | [[0. 0. 1. ... 3. 0. 0.]
[0. 1. 2. ... 1. 0. 1.]
[0. 1. 1. ... 2. 1. 1.]
...
[0. 1. 2. ... 3. 2. 1.]
[0. 1. 1. ... 0. 1. 0.]
[0. 1. 0. ... 3. 0. 1.]]
| MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Solution How would you find the largest change in inflammation for each patient? Does it matter if the change in inflammation is an increase or a decrease? Hint: NumPy has a function called `numpy.absolute()`, | numpy.absolute(data) | _____no_output_____ | MIT | lessons/python/ep1b-plotting-intro.ipynb | emichan14/2019-12-03-intro-to-python-workshop |
Python Basics with Numpy (optional assignment)Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need. **Instructions:**- You will be using Python 3.- Avoid using for-loops and while-loops, un... | import numpy as np
### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test) | test: Hello World
| MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected output**:test: Hello World **What you need to remember**:- Run your cells using SHIFT+ENTER (or "Run cell")- Write code in the designated areas using Python 3 only- Do not modify the code outside of the designated areas 1 - Building basic functions with numpy Numpy is the main package for scientific computi... | # GRADED FUNCTION: basic_sigmoid
import math
def basic_sigmoid(x):
"""
Compute sigmoid of x.
Arguments:
x -- A scalar
Return:
s -- sigmoid(x)
"""
### START CODE HERE ### (≈ 1 line of code)
s = 1 / (1 + math.exp(-x))
### END CODE HERE ###
return s
basic_sigmoid(3... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: ** basic_sigmoid(3) ** 0.9525741268224334 Actually, we rarely use the "math" library in deep learning because the inputs of the functions are real numbers. In deep learning we mostly use matrices and vectors. This is why numpy is more useful. | ### One reason why we use "numpy" instead of "math" in Deep Learning ###
x = [1, 2, 3]
basic_sigmoid(x) # you will see this give an error when you run it, because x is a vector. | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
In fact, if $ x = (x_1, x_2, ..., x_n)$ is a row vector then $np.exp(x)$ will apply the exponential function to every element of x. The output will thus be: $np.exp(x) = (e^{x_1}, e^{x_2}, ..., e^{x_n})$ | import numpy as np
# example of np.exp
x = np.array([1, 2, 3])
print(np.exp(x)) # result is (exp(1), exp(2), exp(3)) | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
Furthermore, if x is a vector, then a Python operation such as $s = x + 3$ or $s = \frac{1}{x}$ will output s as a vector of the same size as x. | # example of vector operation
x = np.array([1, 2, 3])
print (x + 3)
| _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
Any time you need more info on a numpy function, we encourage you to look at [the official documentation](https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.exp.html). You can also create a new cell in the notebook and write `np.exp?` (for example) to get quick access to the documentation.**Exercise**: I... | # GRADED FUNCTION: sigmoid
import numpy as np # this means you can access numpy functions by writing np.function() instead of numpy.function()
def sigmoid(x):
"""
Compute the sigmoid of x
Arguments:
x -- A scalar or numpy array of any size
Return:
s -- sigmoid(x)
"""
### START C... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **sigmoid([1,2,3])** array([ 0.73105858, 0.88079708, 0.95257413]) 1.2 - Sigmoid gradientAs you've seen in lecture, you will need to compute gradients to optimize loss functions using backpropagation. Let's code your first gradient function.**Exercise**: Implement th... | # GRADED FUNCTION: sigmoid_derivative
def sigmoid_derivative(x):
"""
Compute the gradient (also called the slope or derivative) of the sigmoid function with respect to its input x.
You can store the output of the sigmoid function into variables and then use it to calculate the gradient.
Arguments:... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **sigmoid_derivative([1,2,3])** [ 0.19661193 0.10499359 0.04517666] 1.3 - Reshaping arrays Two common numpy functions used in deep learning are [np.shape](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html) and [np.reshape()](https://docs.... | # GRADED FUNCTION: image2vector
def image2vector(image):
"""
Argument:
image -- a numpy array of shape (length, height, depth)
Returns:
v -- a vector of shape (length*height*depth, 1)
"""
### START CODE HERE ### (≈ 1 line of code)
v = image
v = v.reshape(v.shape[0] * v.shap... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **image2vector(image)** [[ 0.67826139] [ 0.29380381] [ 0.90714982] [ 0.52835647] [ 0.4215251 ] [ 0.45017551] [ 0.92814219] [ 0.96677647] [ 0.85304703] [ 0.52351845] [ 0.19981397] [ 0.27417313] [ 0.60659855] [ 0.00533165] [ 0.10820313] [ 0.49978937] [ 0.34144279] [ 0.94630077]... | # GRADED FUNCTION: normalizeRows
def normalizeRows(x):
"""
Implement a function that normalizes each row of the matrix x (to have unit length).
Argument:
x -- A numpy matrix of shape (n, m)
Returns:
x -- The normalized (by row) numpy matrix. You are allowed to modify x.
"""
... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **normalizeRows(x)** [[ 0. 0.6 0.8 ] [ 0.13736056 0.82416338 0.54944226]] **Note**:In normalizeRows(), you can try to print the shapes of x_norm and x, and then rerun the assessment. You'll find out that they have different shapes. This i... | # GRADED FUNCTION: softmax
def softmax(x):
"""Calculates the softmax for each row of the input x.
Your code should work for a row vector and also for matrices of shape (n, m).
Argument:
x -- A numpy matrix of shape (n,m)
Returns:
s -- A numpy matrix equal to the softmax of x, of shape (n,m)
... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **softmax(x)** [[ 9.80897665e-01 8.94462891e-04 1.79657674e-02 1.21052389e-04 1.21052389e-04] [ 8.78679856e-01 1.18916387e-01 8.01252314e-04 8.01252314e-04 8.01252314e-04]] **Note**:- If you print the shapes of x_exp, x_sum and s above and rerun the a... | import time
x1 = [9, 2, 5, 0, 0, 7, 5, 0, 0, 0, 9, 2, 5, 0, 0]
x2 = [9, 2, 2, 9, 0, 9, 2, 5, 0, 0, 9, 2, 5, 0, 0]
### CLASSIC DOT PRODUCT OF VECTORS IMPLEMENTATION ###
tic = time.process_time()
dot = 0
for i in range(len(x1)):
dot+= x1[i]*x2[i]
toc = time.process_time()
print ("dot = " + str(dot) + "\n ----- Comp... | _____no_output_____ | MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
As you may have noticed, the vectorized implementation is much cleaner and more efficient. For bigger vectors/matrices, the differences in running time become even bigger. **Note** that `np.dot()` performs a matrix-matrix or matrix-vector multiplication. This is different from `np.multiply()` and the `*` operator (whic... | # GRADED FUNCTION: L1
def L1(yhat, y):
"""
Arguments:
yhat -- vector of size m (predicted labels)
y -- vector of size m (true labels)
Returns:
loss -- the value of the L1 loss function defined above
"""
### START CODE HERE ### (≈ 1 line of code)
loss = np.sum(np.abs(yhat -... | L1 = 1.1
| MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
**Expected Output**: **L1** 1.1 **Exercise**: Implement the numpy vectorized version of the L2 loss. There are several way of implementing the L2 loss but you may find the function np.dot() useful. As a reminder, if $x = [x_1, x_2, ..., x_n]$, then `np.dot(x,x)` = $\sum_{j=0}^n x_j^{2}$. - ... | # GRADED FUNCTION: L2
def L2(yhat, y):
"""
Arguments:
yhat -- vector of size m (predicted labels)
y -- vector of size m (true labels)
Returns:
loss -- the value of the L2 loss function defined above
"""
### START CODE HERE ### (≈ 1 line of code)
loss = np.sum((y - yhat) **... | L2 = 0.43
| MIT | NNs-and-deep-learning/jupyter/Week 2/Python Basics with Numpy/Python Basics With Numpy v3.ipynb | HaleTom/deeplearning.ai_notes |
Your very own neural networkIn this programming assignment we're going to build a neural network using naught but pure numpy and steel nerves. It's going to be fun, we promise!__Disclaimer:__ This assignment is ungraded. | %%bash
shred -u setup_colab.py
wget https://raw.githubusercontent.com/hse-aml/intro-to-dl-pytorch/main/utils/setup_colab.py -O setup_colab.py
import setup_colab
setup_colab.setup_week02_honor()
import tqdm_utils
from __future__ import print_function
import numpy as np
np.random.seed(42) | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
Here goes our main class: a layer that can do .forward() and .backward() passes. | class Layer:
"""
A building block. Each layer is capable of performing two things:
- Process input to get output: output = layer.forward(input)
- Propagate gradients through itself: grad_input = layer.backward(input, grad_output)
Some layers also have learnable parameters... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
The road aheadWe're going to build a neural network that classifies MNIST digits. To do so, we'll need a few building blocks:- Dense layer - a fully-connected layer, $f(X)=W \cdot X + \vec{b}$- ReLU layer (or any other nonlinearity you want)- Loss function - crossentropy- Backprop algorithm - a stochastic gradient des... | class ReLU(Layer):
def __init__(self):
"""ReLU layer simply applies elementwise rectified linear unit to all inputs"""
pass
def forward(self, input):
"""Apply elementwise ReLU to [batch, input_units] matrix"""
# <your code. Try np.maximum>
def backward(self, input, ... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
Instant primer: lambda functionsIn python, you can define functions in one line using the `lambda` syntax: `lambda param1, param2: expression`For example: `f = lambda x, y: x+y` is equivalent to a normal function:```def f(x,y): return x+y```For more information, click [here](http://www.secnetix.de/olli/Python/lambd... | class Dense(Layer):
def __init__(self, input_units, output_units, learning_rate=0.1):
"""
A dense layer is a layer which performs a learned affine transformation:
f(x) = <W*x> + b
"""
self.learning_rate = learning_rate
# initialize weights with small random n... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
Testing the dense layerHere we have a few tests to make sure your dense layer works properly. You can just run them, get 3 "well done"s and forget they ever existed.... or not get 3 "well done"s and go fix stuff. If that is the case, here are some tips for you:* Make sure you compute gradients for W and b as __sum of ... | l = Dense(128, 150)
assert -0.05 < l.weights.mean() < 0.05 and 1e-3 < l.weights.std() < 1e-1,\
"The initial weights must have zero mean and small variance. "\
"If you know what you're doing, remove this assertion."
assert -0.05 < l.biases.mean() < 0.05, "Biases must be zero mean. Ignore if you have a reason to... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
The loss functionSince we want to predict probabilities, it would be logical for us to define softmax nonlinearity on top of our network and compute loss given predicted probabilities. However, there is a better way to do so.If you write down the expression for crossentropy as a function of softmax logits (a), you'll ... | def softmax_crossentropy_with_logits(logits,reference_answers):
"""Compute crossentropy from logits[batch,n_classes] and ids of correct answers"""
logits_for_answers = logits[np.arange(len(logits)),reference_answers]
xentropy = - logits_for_answers + np.log(np.sum(np.exp(logits),axis=-1))
retu... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
Full networkNow let's combine what we've just built into a working neural network. As we announced, we're gonna use this monster to classify handwritten digits, so let's get them loaded. We will download the data using pythorch. | !pip install torchvision
# import numpy and matplotlib
%pylab inline
import torchvision
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Lambda(lambda x: x.flatten())
])
train_dataset = torchvision.datasets.MNIST(root='.', train=True,
... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
We'll define network as a list of layers, each applied on top of previous one. In this setting, computing predictions and training becomes trivial. | network = []
network.append(Dense(X_train.shape[1],100))
network.append(ReLU())
network.append(Dense(100,200))
network.append(ReLU())
network.append(Dense(200,10))
def forward(network, X):
"""
Compute activations of all network layers by applying them sequentially.
Return a list of activations for each laye... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
Instead of tests, we provide you with a training loop that prints training and validation accuracies on every epoch.If your implementation of forward and backward are correct, your accuracy should grow from 90~93% to >97% with the default network. Training loopAs usual, we split data into minibatches, feed each such m... | def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.random.permutation(len(inputs))
for start_idx in tqdm_utils.tqdm_notebook_failsafe(range(0, len(inputs) - batchsize + 1, batchsize)):
if shuffle:
excerp... | _____no_output_____ | MIT | 02_PYTHON/week02/week02_numpy_neural_network.ipynb | milaan9/Deep_Learning_Algorithms_from_Scratch |
E2E ML on GCP: MLOps stage 4 : formalization: get started with Google Artifact Registry View on GitHub Open in Vertex AI Workbench OverviewThis tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 4 : formalization: ... | ONCE_ONLY = False
if ONCE_ONLY:
! pip3 install -U tensorflow==2.5 $USER_FLAG
! pip3 install -U tensorflow-data-validation==1.2 $USER_FLAG
! pip3 install -U tensorflow-transform==1.2 $USER_FLAG
! pip3 install -U tensorflow-io==0.18 $USER_FLAG
! pip3 install --upgrade google-cloud-aiplatform[tensorboa... | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Restart the kernelOnce you've installed the additional packages, you need to restart the notebook kernel so it can find the packages. | import os
if not os.getenv("IS_TESTING"):
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True) | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Set your project ID**If you don't know your project ID**, you may be able to get your project ID using `gcloud`. | PROJECT_ID = "[your-project-id]" # @param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
# Get your GCP project id from gcloud
shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID:"... | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
RegionYou can also change the `REGION` variable, which is used for operationsthroughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.- Americas: `us-central1`- Europe: `europe-west4`- Asia Pacific: `asia-east1`You may not use a multi-regi... | REGION = "us-central1" # @param {type: "string"} | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
TimestampIf you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial. | from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S") | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Set up variablesNext, set up some variables used throughout the tutorial. Import libraries and define constants Introduction to Google Artifact RegistryThe `Google Artifact Registry` is a service for storing and managing artifacts in private repositories, including container images, Helm charts, and language packages... | ! gcloud services enable artifactregistry.googleapis.com | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Create a private Docker repositoryYour first step is to create your own Docker repository in Google Artifact Registry.1. Run the `gcloud artifacts repositories create` command to create a new Docker repository with your region with the description "docker repository".2. Run the `gcloud artifacts repositories list` com... | PRIVATE_REPO = "my-docker-repo"
! gcloud artifacts repositories create {PRIVATE_REPO} --repository-format=docker --location={REGION} --description="Docker repository"
! gcloud artifacts repositories list | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Configure authentication to your private repoBefore you push or pull container images, configure Docker to use the `gcloud` command-line tool to authenticate requests to `Artifact Registry` for your region. | ! gcloud auth configure-docker {REGION}-docker.pkg.dev --quiet | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Obtain an example container imageFor demonstration purposes, you obtain (pull) a local copy of our demonstration container image: `hello-app:1.0` | ! docker pull us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0 | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Tagging your container imageNow that you have your own container image, the first step is to tag your image.- Tagging the Docker image with a repository name configures the docker push command to push the image to a specific location, e.g., us-central1-docker.pkg.dev.- `:my-tag` is a tag you're adding to the Docker im... | CONTAINER_NAME = "my-image:my-tag"
! docker tag us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0 us-central1-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME} | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Push your image to your private Docker repositoryNext, push your container to your private Docker repository. | ! docker push {REGION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME} | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Pull your image from your private Docker repostoryNow pull your container from your private Docker repository. | ! docker pull {REGION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME} | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Deleting your private Docker repostoryFinally, once your private repository becomes obsolete, use the command `gcloud artifacts repositories delete` to delete it `Google Artifact Registry`. | ! gcloud artifacts repositories delete {PRIVATE_REPO} --location={REGION} --quiet | _____no_output_____ | Apache-2.0 | notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb | prodonjs/vertex-ai-samples |
Introduzione pratica ai tipi di dati in PythonPer ulteriori esempi e riferimenti, si veda [An Informal Introduction to Python](https://docs.python.org/2/tutorial/introduction.html) | metadata = "Name;Identity;Birth place;Publisher;Height;Weight;Gender;First appearance;Eye color;Hair color;Strength;Intelligence"
rowdata = 'Silver Surfer;Norrin Radd;Zenn-La;Marvel Comics;193.00999999999999;101.34;M;;White;No Hair;100;average' | _____no_output_____ | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
StringheLe stringhe in Python sono collezioni di caratteri incluse fra i simboli "..." o '...'. Le due notazioni non hanno significative differenze se usiamo "..." non dobbiamo quotare il simbolo ' e viceversa. | print 'Ero "felice"'
print 'Ero \'felice\''
print "Ero 'felice'"
print "Ero \"felice\"\nDavvero!" | Ero "felice"
Ero 'felice'
Ero 'felice'
Ero "felice"
Davvero!
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Per evitare di interpretare il simbolo \ come un marcatore possiamo ricorrere alle 'raw strings'. In generale, le stringhe Python possono essere qialificate come stringhe speciali, anteponendo alla stringa un descrittore. Per conoscere il tipo di un dato esiste la funzione `type`. | print 'Bianco\nero'
print r'Bianco\nero'
print type('Bianco\nero'), type(r'Bianco\nero'), type(u'Bianco\nero') | Bianco
ero
Bianco\nero
<type 'str'> <type 'str'> <type 'unicode'>
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Stringhe che occupano più linee e conservano la formattazione sono denotate dai simboli """...""" o '''...'''. | silverbio = """
Silver Surfer, alter ego di Norrin Radd, è un personaggio immaginario dei fumetti
creato da Stan Lee e Jack Kirby nel 1966 \
e pubblicato dalla casa editrice statunitense Marvel Comics.
Esordisce nella serie The Fantastic Four (Vol. 1[1]) n. 48 del 1966, nella Trilogia di Galactus,
\
il cui riscontro ... |
Silver Surfer, alter ego di Norrin Radd, è un personaggio immaginario dei fumetti
creato da Stan Lee e Jack Kirby nel 1966 e pubblicato dalla casa editrice statunitense Marvel Comics.
Esordisce nella serie The Fantastic Four (Vol. 1[1]) n. 48 del 1966, nella Trilogia di Galactus,
il cui riscontro positivo da parte d... | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Concatenazione di stringheLe stringhe si concatenano con l'operatore `+`, si ripetono con `*`. I 'string literals' (le stringhe con '...' si concatenano anche solo giustapponendole | print 3 * "super " + "silver"
print 'con' 'catenato'
sentence = ('Se uso le parentesi e '
'i literals '
'è super comodo!')
print sentence | super super super silver
concatenato
Se uso le parentesi e i literals è super comodo!
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Ma non si possono concatenare variabili e literals! | one = 'a '
print one 'string'
print one + 'string'
print 1 + 'string' | a string
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Indicizzazione delle stringheI caratteri di una stringa sono indicizzati e accessibili come in una lista, secondo lo schema:|S|i|l|v|e|r||:---:|:---:|:---:|:---:|:---:|:---:||0|1|2|3|4|5||-6|-5|-4|-3|-2|-1| | silver = 'Silver'
print len(silver)
print silver[0]
print silver[5]
print silver[-1]
print silver[-6]
print silver[6] | 6
S
r
r
S
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Possiamo perciò usare con le stringhe una tecnica molto usata anche per le liste e fondamentale in Python: lo 'slicing' | print silver[2:]
print silver[:3]
print silver[2:4]
print silver[3:22]
print silver[:-2] | lver
Sil
lv
ver
Silv
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Tuttavia, a differenza delle liste, le stringhe python sono **immutabili** | silver[1] = 'o'
solver = silver[:1] + 'o' + silver[2:]
print solver
print " - ; | ".join(['A', 'B', 'C']) | A - ; | B - ; | C
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Metodi di utilità per le stringheIn Python, le stringhe sono oggetti dotati di un'ampia gamma di metodi per diverse funzioni (vedi [string methods](https://docs.python.org/2/library/stdtypes.htmlstring-methods)): | print 'find()', '->', silver.find('ver')
print 'endswith()/startswith()', '->', silver.endswith('er'), silver.startswith('er')
print 'lower()', '->', silver.lower()
print 'lstrip()/rstrip()', '->', silver.lstrip('S'), silver.rstrip('S')
print 'replace()', '->', silver.replace('er', 'an')
print 'split()', '->', silver.s... | find() -> 3
endswith()/startswith() -> True False
lower() -> silver
lstrip()/rstrip() -> ilver Silver
replace() -> Silvan
split() -> ['Sil', 'er']
upper() -> SILVER
join() -> A Silver hero
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Formattazione di stringheLe stringhe offrono anche l'operatore `%` (modulo). Si tratta di un operstore che consente di formattare e interpolare una stringa con diversi tipi di dato. | print u"%(superhero)s è stato creato da %(creator)s nel %(year)i" % {
'superhero': 'Silver Surfer', 'creator': ' e '.join(['Stan Lee', 'Jack Kirby']), 'year': 1966
}
print u"{} è stato creato da {} nel {}".format('Silver Surfer', ' e '.join(['Stan Lee', 'Jack Kirby']), 1966) | _____no_output_____ | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
UnicodeIn Python 2.* le stringhe sono intese come succesione di caratteri ASCII dove non esplicitamente codificate per mezzo dei metodi `encode()` e `decode()`. Le stringhe `unicode` vanno dichiarate come tali col carattere `u`. | u = u'Silver Surfer è nato a Zenn-La'
u
str(u)
u.encode('utf-8')
str(u.encode('utf-8')) | _____no_output_____ | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
NumeriIn Python ci sono 4 tipi numerici principali: `plain integers`, `long integers`, `floating point numbers`, e `complex numbers`. I booleani sono un sottotipo di interi. - I `plain integers` hanno sempre almeno 32 bit di precisione (l'intero massimo è `sys.maxint` e il minimo `-sys.maxint - 1`). - I `Long integers... | import sys
print sys.maxint
print sys.float_info | _____no_output_____ | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Operatori aritmetici | print 2 + 2
print 2 * 3
print 17 / 3
print 17 / 3.0
print 17 // 3.0
print 17 % 3
print 17 ** 3 | 4
6
5
5.66666666667
5.0
2
4913
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Conversioni | a, b, c = 2, 3.0, 3.5
print float(a), int(b), int(c), str(c), bool(c), bool(c - 3.5) | 2.0 3 3 3.5 True False
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Gestione delle dateLe principali funzionalità per la gestione delle date sono incluse nei moduli `datetime`, `time` e `calendar` che si basano sullo standard Coordinated Universal Time (UTC). Le date sono **immutabili**.- `datetime.date` : date secondo il calendario gregoriano- `datetime.time` : tempo, considerando pe... | import datetime as dtt
import time
when = dtt.date(1966, 4, 28)
now = dtt.time(16, 36)
temp = dtt.datetime(when.year, when.month, when.day, now.hour, now.minute, now.second, now.microsecond)
today = dtt.datetime.today()
print when.day
print when.month
print when.year
print now.hour, now.minute, now.second, now.microsec... | -18971 days, 4:40:04.205052 <type 'datetime.timedelta'> -1639077595.79
(2018, 14, 5)
2018-04-06 11:55:55.794948
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Conversione tra stringhe e date`strftime()` e `strptime()` convertono date in stringhe e viceversa secondo la seguente convenzione di formato.DirectiveMeaningExample%aWeekday as locale’sabbreviated name.Sun, Mon, …, Sat(en_US);So, Mo, …, Sa(de_DE)%AWeekday as locale’s full name.Sunday, Monday, …,Saturday (en_US);Sonnt... | print dtt.datetime.strftime(today, "%d/%m/%Y %I:%M:%S %p %z")
from_string = dtt.datetime.strptime('6/12/1978 12:36:46',
'%d/%m/%Y %H:%M:%S')
print from_string | 1978-12-06 12:36:46
| Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Put things together | silver_data = rowdata.split(';')
print metadata.split(';')
print silver_data | _____no_output_____ | Apache-2.0 | L02-datatypes.ipynb | dariomalchiodi/python-DS4EBF |
Init Install packages | !pip install spacy
!pip3 uninstall --quiet --yes tensorflow
!pip3 install --quiet tensorflow-gpu==1.14.0
!pip3 install --quiet tensorflow-hub
!pip3 install --quiet sentencepiece==0.1.83
!pip3 install --quiet tf-sentencepiece==0.1.83
!pip3 install --quiet simpleneighbors
| Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.1.8)
Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)
Requirement already satisfied: preshed<2.1.0,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.... | Unlicense | qa.ipynb | thingumajig/qa-prototype |
Initialize grammar parser | import spacy
nlp = spacy.load("en_core_web_sm")
# doc = nlp("With respect to all losses caused by the peril of Flood, the Company shall not be liable, in the aggregate for any one Policy year, for more than its proportionate share of US$25,000,000.")
# doc = nlp("The Program limit of liability is US$400,000,000.")
# pr... | _____no_output_____ | Unlicense | qa.ipynb | thingumajig/qa-prototype |
Set up Tensorflow graph | %%time
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import tf_sentencepiece
# Set up graph.
g = tf.Graph()
with g.as_default():
questions_input = tf.placeholder(dtype=tf.string, shape=[None])
responses_input = tf.placeholder(dtype=tf.string, shape=[None])
contexts_input = tf.placeholde... | /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dis... | Unlicense | qa.ipynb | thingumajig/qa-prototype |
Run Initialize tensorflow session. | %%time
# Initialize session.
session = tf.Session(graph=g)
session.run(init_op) | CPU times: user 10.5 s, sys: 2 s, total: 12.5 s
Wall time: 14.3 s
| Unlicense | qa.ipynb | thingumajig/qa-prototype |
Tests | # %%time
sentences = '''
F. PROGRAM LIMITS OF LIABILITY
(1) The Program limit of liability is US$400,000,000.
(2) Sublimits below are applicable to all direct physical loss, damage or destruction insured against, except an Accident.
(a) With respect to all losses caused by the peril of Flood, the Company shall not... | Shape: (10, 512)
Candidates:
respect to all losses caused by the peril of Earthquake
all losses caused by the peril of Earthquake
the peril of Earthquake
Earthquake
the Company
the aggregate for any one Policy year
any one Policy year
more than its proportionate share of US$ 50,000,000
its proportionate share ... | Unlicense | qa.ipynb | thingumajig/qa-prototype |
Form | %%time
#@title Question and answer
text = "\u0410\u0441\u0442\u0440\u043E\u043D\u043E\u043C\u044B \u0418\u043D\u0441\u0442\u0438\u0442\u0443\u0442\u0430 \u0432\u043D\u0435\u0437\u0435\u043C\u043D\u043E\u0439 \u0444\u0438\u0437\u0438\u043A\u0438 \u041E\u0431\u0449\u0435\u0441\u0442\u0432\u0430 \u041C\u0430\u043A\u0441\... | Shape: (110, 512)
Candidates:
Астрономы
Института
внеземной
физики
Общества
Макса
Планка
в
Германии
сообщили
о
всплеске
активности
неизвестного
источника
рентгеновских
лучей
в
галактике
NGC
300,
расположенной
в
семи
миллионах
световых
лет
от
Земли.
Астрономы Института
Института внеземной
... | Unlicense | qa.ipynb | thingumajig/qa-prototype |
DAGVIZ Metro styling optionsThis notebook demonstrates the various Metro styling options.In order to apply styling we need to call `render_svg` by hand with the appropriate renderer and style configuration. | from dagviz import render_svg
from dagviz.style.metro import svg_renderer, StyleConfig
from IPython.display import HTML
import networkx as nx | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
First we construct a simple graph that demonstrates all the visual aspects a rendering may have. | g = nx.DiGraph()
g.add_node("a", label="switch(value)")
g.add_node("b", label="case 1")
g.add_node("c", label="case 2")
g.add_node("d", label="case 3")
g.add_node("e", label="end")
g.add_edge("a", "b")
g.add_edge("a", "c")
g.add_edge("a", "d")
g.add_edge("b", "e")
g.add_edge("c", "e")
g.add_edge("d", "e") | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Default renderingWithout any configuration, we get (of course) the default rendering: | HTML(render_svg(g)) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
ScaleThe *scale* setting determines the amount of space each node has: | HTML(render_svg(g, style=svg_renderer(StyleConfig(scale=20)))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Node radiusThe *node radius* determines the size of the bubbles: | HTML(render_svg(g, style=svg_renderer(StyleConfig(node_radius=10)))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Node fillBy default the node fill color is automatically selected. This can be overriden by specifying a fixed color: | HTML(render_svg(g, style=svg_renderer(StyleConfig(node_fill="black")))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Node strokeThe *node stroke* specifies the color of the border of the bubble: | HTML(render_svg(g, style=svg_renderer(StyleConfig(node_stroke="black")))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Node stroke widthThe *node stroke width* determines the width of the border of the bubbles: | HTML(render_svg(g, style=svg_renderer(StyleConfig(node_stroke="black", node_stroke_width=4)))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Edge stroke widthThe *edge stroke width* determines the width of the edges: | HTML(render_svg(g, style=svg_renderer(StyleConfig(node_fill="black", edge_stroke_width=10)))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Label font familyThe default font family for labels is "sans-serif". This can be changes too: | HTML(render_svg(g, style=svg_renderer(StyleConfig(label_font_family="serif")))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Label arrow strokeThe *label arrow stroke* determines the color of the line from node to label: | HTML(render_svg(g, style=svg_renderer(StyleConfig(label_arrow_stroke="black")))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Label arrow dash arrayThe *label arrow dash array* determines how the label arrow is dashed: | HTML(render_svg(g, style=svg_renderer(StyleConfig(label_arrow_dash_array="0")))) | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Arc radiusThe *arc radius* determines the radius of the arc from vertical line to a node: | HTML(render_svg(g, style=svg_renderer(StyleConfig(arc_radius=5))))
[".."]*4
"../"*4 | _____no_output_____ | Apache-2.0 | notebooks/Metro styling.ipynb | WimYedema/dagviz |
Copyright 2020 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en-snapshot/addons/tutorials/time_stopping.ipynb | ilyaspiridonov/docs-l10n |
TensorFlow Addons Callbacks: TimeStopping View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook OverviewThis notebook will demonstrate how to use TimeStopping Callback in TensorFlow Addons. Setup | import tensorflow_addons as tfa
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten | _____no_output_____ | Apache-2.0 | site/en-snapshot/addons/tutorials/time_stopping.ipynb | ilyaspiridonov/docs-l10n |
Import and Normalize Data | # the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0 | Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
| Apache-2.0 | site/en-snapshot/addons/tutorials/time_stopping.ipynb | ilyaspiridonov/docs-l10n |
Build Simple MNIST CNN Model | # build the model using the Sequential API
model = Sequential()
model.add(Flatten(input_shape=(28, 28)))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metr... | _____no_output_____ | Apache-2.0 | site/en-snapshot/addons/tutorials/time_stopping.ipynb | ilyaspiridonov/docs-l10n |
Simple TimeStopping Usage | # initialize TimeStopping callback
time_stopping_callback = tfa.callbacks.TimeStopping(seconds=5, verbose=1)
# train the model with tqdm_callback
# make sure to set verbose = 0 to disable
# the default progress bar.
model.fit(x_train, y_train,
batch_size=64,
epochs=100,
callbacks=[time_s... | Train on 60000 samples, validate on 10000 samples
Epoch 1/100
60000/60000 [==============================] - 5s 81us/sample - loss: 0.3432 - accuracy: 0.9003 - val_loss: 0.1601 - val_accuracy: 0.9529
Epoch 2/100
60000/60000 [==============================] - 4s 67us/sample - loss: 0.1651 - accuracy: 0.9515 - val_loss: ... | Apache-2.0 | site/en-snapshot/addons/tutorials/time_stopping.ipynb | ilyaspiridonov/docs-l10n |
Change to Quest for Orthologs 2019 data directory | cd ~/data_sm/kmer-hashing/quest-for-orthologs/data/2019/
ls -lha | total 2.6G
drwxr-xr-x 5 olga root 4.0K Jan 10 08:02 [0m[01;34m.[0m/
drwxr-xr-x 3 olga root 4.0K Dec 25 17:48 [01;34m..[0m/
drwxr-xr-x 5 olga czb 4.0K Dec 26 19:44 [01;34mArchaea[0m/
drwxr-xr-x 5 olga czb 16K Dec 26 19:44 [01;34mBacteria[0m/
drwxr-xr-x 8 olga czb 32K Jan 8 08:13 [01;34mEukaryota[0m/
-rw... | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Get Retinal gene names from | s = 'RHO GNAT1 GNB1 GNGT1 OPN1SW OPN1MW GNAT2 GNB3 GNGT2 PDE6A PDE6B PDE6G PDE6C PDE6H SAG ARR3 RGS9 CNGA1 CNGA3 CNGB1 CNGB3 GRK1 GRK7 RCVRN GUCA1A GUCA1B GUCY2D GUCY2F'
genes = s.split()
genes
len(genes)
ensembl_rest.lookup_post(genes[0])
s = 'P08100 P11488 P62873 P63211 P03999 P04001 P19087 P16520 O14610 PDE6A PDE6B... | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Read Human ID mapping | human_id_mapping = pd.read_csv('Eukaryota/UP000005640_9606.idmapping', sep='\t', header=None, names=['uniprot_id', 'id_type', 'db_id'])
human_id_mapping.columns = 'source__' + human_id_mapping.columns
print(human_id_mapping.shape)
human_id_mapping.head() | (2668934, 3)
| MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Extract with gene symbsls | human_id_mapping_symbols = human_id_mapping.query('source__db_id in @genes_to_ids.symbol')
print(human_id_mapping_symbols.shape)
human_id_mapping_symbols.head()
human_id_mapping_symbols.source__db_id.nunique() | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Extract with uniprot ids | human_id_mapping_uniprot = human_id_mapping.query('source__db_id in @genes_to_ids.uniprot_id')
print(human_id_mapping_uniprot.shape)
human_id_mapping_uniprot.head()
"RHO" in human_id_mapping.source__db_id.values | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Get ENSMBL ids | visual_uniprot_ids = set(human_id_mapping_uniprot.source__uniprot_id) | set(human_id_mapping_symbols.source__uniprot_id)
len(visual_uniprot_ids)
human_id_mapping_visual_system = human_id_mapping.query('source__uniprot_id in @visual_uniprot_ids')
human_id_mapping_visual_system.head() | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Concatenate all human mappings | human_retina_ids = pd.concat([human_id_mapping_symbols, human_id_mapping_uniprot], ignore_index=True)
human_retina_ids = human_retina_ids.drop_duplicates()
print(human_retina_ids.shape)
human_retina_ids.head() | (202, 3)
| MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Write human retinal genes with uniprot IDs to disk | pwd
human_id_mapping_visual_system.to_csv("human_visual_transduction_with_uniprot_ids.csv", index=False)
human_id_mapping_visual_system.to_csv("human_visual_transduction_with_uniprot_ids.csv.gz", index=False)
human_id_mapping_visual_system.to_parquet("human_visual_transduction_with_uniprot_ids.parquet", index=False) | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Read human proteins and subset to human tfs | retinal_uniprot = set(human_retina_ids.source__uniprot_id)
len(retinal_uniprot)
tf_records = []
for filename in glob.iglob('Eukaryota/human-protein-fastas/*.fasta'):
with screed.open(filename) as records:
for record in records:
name = record['name']
record_id = name.split()[0]
... | _____no_output_____ | MIT | notebooks/521_subset_human_retina_genes.ipynb | czbiohub/kh-analysis |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.