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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mit [SpaCy-jPTDP](https://github.com/KoichiYasuoka/spaCy-jPTDP) | !pip install deplacy spacy_jptdp
import spacy_jptdp
nlp=spacy_jptdp.load("de_gsd")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [Turku-neural-parser-pipeline](https://turkunlp.org/Turku-neural-parser-pipeline/) | !pip install deplacy ufal.udpipe configargparse 'tensorflow<2' torch==0.4.1 torchtext==0.3.1 torchvision==0.2.1
!test -d Turku-neural-parser-pipeline || git clone --depth=1 https://github.com/TurkuNLP/Turku-neural-parser-pipeline
!cd Turku-neural-parser-pipeline && git submodule update --init --recursive && test -d mod... | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [NLP-Cube](https://github.com/Adobe/NLP-Cube) | !pip install deplacy nlpcube
from cube.api import Cube
nlp=Cube()
nlp.load("de")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [Spacy-udpipe](https://github.com/TakeLab/spacy-udpipe) | !pip install deplacy spacy-udpipe
import spacy_udpipe
spacy_udpipe.download("de")
nlp=spacy_udpipe.load("de")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [SpaCy-COMBO](https://github.com/KoichiYasuoka/spaCy-COMBO) | !pip install deplacy spacy_combo
import spacy_combo
nlp=spacy_combo.load("de_gsd")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [Trankit](https://github.com/nlp-uoregon/trankit) | !pip install deplacy trankit transformers
import trankit
nlp=trankit.Pipeline("german")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [Spacy](https://spacy.io/) | !pip install deplacy
!python -m spacy download de_core_news_sm
import de_core_news_sm
nlp=de_core_news_sm.load()
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
Solving the Stefan problem with finite elements This Jupyter notebook shows how to solve the Stefan problem with finite elements and goal-oriented adaptive mesh refinement (AMR) using FEniCS. Python packagesImport the Python packages for use in this notebook. We use the finite element method library FEniCS. | import fenics | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
|Note||----|| This Jupyter notebook server is using FEniCS 2017.2.0 from ppa:fenics-packages/fenics, installed via `apt` on Ubuntu 16.04.|FEniCS has convenient plotting features that don't require us to import `matplotlib`; but using `matplotlib` directly will allow us to annotate the plots. | import matplotlib | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Tell this notebook to embed graphical outputs from `matplotlib`, includings those made by `fenics.plot`. | %matplotlib inline | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
We will also use numpy. | import numpy | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Nomenclature||||-|-||$\mathbf{x}$| point in the spatial domain||$t$| time ||$T = T(\mathbf{x},t)$| temperature field ||$\phi$ | solid volume fraction ||$()_t = \frac{\partial}{\partial t}()$| time derivative ||$T_r$| central temperature of the regularization ||$r$| smoothing parameter of the regularization ||$\mathrm{... | def semi_phase_field(T, T_r, r):
return 0.5*(1. + numpy.tanh((T_r - T)/r))
regularization_central_temperature = 0.
temperatures = numpy.linspace(
regularization_central_temperature - 0.5,
regularization_central_temperature + 0.5,
1000)
legend_strings = []
for regluarization_smoothing_parameter... | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Mesh Define a fine mesh to capture the rapid variation in $\phi(T)$. | N = 1000
mesh = fenics.UnitIntervalMesh(N) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Finite element function space, test function, and solution function Lets use piece-wise linear elements. | P1 = fenics.FiniteElement('P', mesh.ufl_cell(), 1) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
|Note||----||`fenics.FiniteElement` requires the `mesh.ufl_cell()` argument to determine some aspects of the domain (e.g. that the spatial domain is two-dimensional).| Make the finite element function space $V$, which enumerates the finite element basis functions on each cell of the mesh. | V = fenics.FunctionSpace(mesh, P1) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Make the test function $\psi \in \mathbf{V}$. | psi = fenics.TestFunction(V) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Make the solution function $T \in \mathbf{V}$. | T = fenics.Function(V) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Benchmark parameters Set the Stefan number, density, specific heat capacity, and thermal diffusivity. For each we define a `fenics.Constant` for use in the variational form so that FEniCS can more efficiently compile the finite element code. | stefan_number = 0.045
Ste = fenics.Constant(stefan_number) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Define the regularized semi-phase-field for use with FEniCS. | regularization_central_temperature = 0.
T_r = fenics.Constant(regularization_central_temperature)
regularization_smoothing_parameter = 0.005
r = fenics.Constant(regularization_smoothing_parameter)
tanh = fenics.tanh
def phi(T):
return 0.5*(1. + fenics.tanh((T_r - T)/r)) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Furthermore the benchmark problem involves hot and cold walls with constant temperatures $T_h$ and $T_c$, respectively. | hot_wall_temperature = 1.
T_h = fenics.Constant(hot_wall_temperature)
cold_wall_temperature = -0.01
T_c = fenics.Constant(cold_wall_temperature) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Time discretization To solve the initial value problem, we will prescribe the initial values, and then take discrete steps forward in time which solve the governing equations.We set the initial values such that a small layer of melt already exists touching the hot wall.\begin{align*} T^0 = \begin{cases} ... | initial_melt_thickness = 10./float(N)
T_n = fenics.interpolate(
fenics.Expression(
"(T_h - T_c)*(x[0] < x_m0) + T_c",
T_h = hot_wall_temperature,
T_c = cold_wall_temperature,
x_m0 = initial_melt_thickness,
element = P1),
V) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Let's look at the initial values now. | fenics.plot(T_n)
matplotlib.pyplot.title(r"$T^0$")
matplotlib.pyplot.xlabel("$x$")
matplotlib.pyplot.show()
fenics.plot(phi(T_n))
matplotlib.pyplot.title(r"$\phi(T^0)$")
matplotlib.pyplot.xlabel("$x$")
matplotlib.pyplot.show() | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
|Note||----||$\phi$ undershoots and overshoots the expected minimum and maximum values near the rapid change. This is a common feature of interior layers in finite element solutions. Here, `fenics.plot` projected $phi(T^0)$ onto a piece-wise linear basis for plotting. This could suggest we will encounter numerical issu... | timestep_size = 1.e-2
Delta_t = fenics.Constant(timestep_size)
T_t = (T - T_n)/Delta_t
phi_t = (phi(T) - phi(T_n))/Delta_t | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Variational form To obtain the finite element weak form, we follow the standard Ritz-Galerkin method. Therefore, we multiply the strong form *from the left* by the test function $\psi$ from the finite element function space $V$ and integrate over the spatial domain $\Omega$. This gives us the variational problem: Find... | dot, grad = fenics.dot, fenics.grad
F = (psi*(T_t - 1./Ste*phi_t) + dot(grad(psi), grad(T)))*fenics.dx | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
LinearizationNotice that $\mathcal{F}$ is a *nonlinear* variational form. FEniCS will solve the nonlinear problem using Newton's method. This requires computing the Jacobian (formally the Gâteaux derivative) of the nonlinear variational form, yielding a a sequence of linearized problems whose solutions may converge to... | JF = fenics.derivative(F, T, fenics.TrialFunction(V)) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Boundary conditions We need boundary conditions before we can define a variational *problem* (i.e. in this case a boundary value problem).We consider a constant hot temperature on the left wall, a constant cold temperature on the right wall. Because the problem's geometry is simple, we can identify the boundaries with... | hot_wall = "near(x[0], 0.)"
cold_wall = "near(x[0], 1.)" | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Define the boundary conditions for FEniCS. | boundary_conditions = [
fenics.DirichletBC(V, hot_wall_temperature, hot_wall),
fenics.DirichletBC(V, cold_wall_temperature, cold_wall)] | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
The variational problem Now we have everything we need to define the variational problem for FEniCS. | problem = fenics.NonlinearVariationalProblem(F, T, boundary_conditions, JF) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
The benchmark solution Finally we instantiate the adaptive solver with our problem and goal | solver = fenics.NonlinearVariationalSolver(problem) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
and solve the problem to the prescribed tolerance. | solver.solve() | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
|Note||----||`solver.solve` will modify the solution `w`, which means that `u` and `p` will also be modified.| Now plot the temperature and solid volume fraction. | def plot(T):
fenics.plot(T)
matplotlib.pyplot.title("Temperature")
matplotlib.pyplot.xlabel("$x$")
matplotlib.pyplot.ylabel("$T$")
matplotlib.pyplot.show()
fenics.plot(phi(T))
matplotlib.pyplot.title("Solid volume fraction")
matplotlib.pyplot.xlabel("$x$")
matpl... | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Let's run further. | for timestep in range(10):
T_n.vector()[:] = T.vector()
solver.solve()
plot(T) | _____no_output_____ | MIT | tutorials/FEniCS/00-StefanProblem.ipynb | yoczhang/phaseflow-fenics |
Simulate straight line and circular movements with Bicycle modelRobot is at the origin (0, 0) and facing North, i.e, $\theta = \pi/2$. Assume the wheelbase of the vehicle $L$ = 0.9 m |
#uncomment this decorator to test your code
@test
def bicycle_model(curr_pose, v, delta, dt=1.0):
'''
>>> bicycle_model((0.0,0.0,0.0), 1.0, 0.0)
(1.0, 0.0, 0.0)
>>> bicycle_model((0.0,0.0,0.0), 0.0, np.pi/4)
(0.0, 0.0, 0.0)
>>> bicycle_model((0.0, 0.0, 0.0), 1.0, np.pi/4)
(1.0, 0.0, 1.11)
'''
# writ... | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Simulate Bicycle model with Open Loop controlWe want the robot to follow these instructions**straight 10m, right turn, straight 5m, left turn, straight 8m, right turn**It is in open loop; control commands have to be calculated upfront. How do we do it?To keep things simple in the first iteration, we can fix $v = v_c$ ... | v_c = 1 # m/s
delta_c = np.pi/6 # rad/s
#calculate time taken to finish a quarter turn (pi/2)
# unlike you would need to take into account v_c and L of the vehicle as well
t_turn = int(np.pi/2/delta_c)
#calculate the time taken to finish straight segments
# omega array is to be padded with equivalent zeros
t_straigh... | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Let us make a cool function out of this!Take in as input a generic route and convert it into open-loop commandsInput format: [("straight", 5), ("right", 90), ("straight", 6), ("left", 85)]Output: all_v, all_delta | def get_open_loop_commands(route, vc=1, deltac=np.pi/12):
all_delta = []
for dir, command in route:
if dir == 'straight':
t_straight = np.ceil(command/vc).astype('int')
all_delta += [0]*t_straight
elif dir == 'right':
all_delta += [-deltac]*np.ceil(np.deg2rad(command)/deltac... | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Unit test your function with the following inputs+ [("straight", 5), ("right", 90), ("straight", 6), ("left", 85)]+ $v_c = 1$+ $delta_c = \pi/12$ | v, delta = get_open_loop_commands([("straight", 5), ("right", 90), ("straight", 6), ("left", 85)], 1, np.pi/12)
robot_trajectory = []
all_v, all_delta = get_open_loop_commands([("straight", 5), ("right", 90), ("straight", 6), ("left", 85)])
pose = np.array([0, 0, np.pi/2])
for v, delta in zip(all_v, all_delta):
#in... | /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the fu... | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Shape the turnLet us try something cooler than before (though a bit tricky in open loop). Instead of boring circular arcs, change the steering angle so that the robot orientation changes as shown in the equation below$\theta = (\theta_i - \theta_f) * (1 - 3x^2 + 2\theta^3) + \theta_f \thinspace \vee x \in [0,1]$First... | def poly_turn(theta_i, theta_f, n=10):
x = np.linspace(0, 1, num=n)
return (theta_i-theta_f) * (1 - 3 * x * x + 2 * (x**3)) + theta_f | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
How does a right turn look? | plt.figure()
plt.plot(poly_turn(np.pi/2, 0),'.')
plt.plot(poly_turn(np.pi/2, 0)) | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Now plot a right turn (North to East) | plt.figure()
plt.plot(poly_turn(np.pi/2, np.pi),'.')
plt.plot(poly_turn(np.pi/2, np.pi)) | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
How does $\theta$ change when we had constant $\delta$? Plot it | theta_change = np.diff(poly_turn(np.pi/2, np.pi))
plt.plot(theta_change,'.')
plt.plot(theta_change) | _____no_output_____ | MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
We know the rate of change of $\theta$ is proportional to $\delta$. Can you work out the sequence of $\delta$ to change $\theta$ as in the cubic polynomial shown above? | L = 0.9
v = 1
theta_change = np.diff(poly_turn(np.pi/2, np.pi))
delta = np.arctan((L/v)*theta_change)
print(delta) | [0.04844344 0.12920623 0.18593495 0.21942362 0.23048008 0.21942362
0.18593495 0.12920623 0.04844344]
| MIT | week1/maithili/Q3 - Q/Attempt1_filesubmission_bicycle_model.ipynb | naveenmoto/lablet102 |
Table of Contents 1 Introduction2 Imports3 Load the final text cleancat15 data4 Plot g00 vs g20 Kernel Author: Bhishan Poudel, Ph.D Contd. Astrophysics Date: Jan 10, 2020 Update: Jan 13, 2020 IntroductionDate: Dec 10, 2019 Mon**Update** 1. Looked at gm0 vs gc0... | import json, os,sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
import plotly
import ipywidgets
pd.set_option('display.max_columns',200)
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
print([(x.__name__, x.__version__) for x in [np,pd,sns,plotly... | _____no_output_____ | Apache-2.0 | Jan_2020/a03_jan13/a01_cleancat15_gc0_gm0.ipynb | bpRsh/shear_analysis_after_dmstack |
Load the final text cleancat15 data```g_sq = g00 g00 + g10 g10gmd_sq = gmd0**2 + gmd1**2``` | !head -2 ../data/cleancat/final_text_cleancat15_000_167.txt
names = "fN[0][0] fN[1][0] fN[2][0] fN[3][0] id[0][0] id[1][0] id[2][0] id[3][0] x[0] x[1] errx[0][0] errx[0][1] errx[1][0] errx[1][1] errx[2][0] errx[2][1] errx[3][0] ... | (90623, 50)
| Apache-2.0 | Jan_2020/a03_jan13/a01_cleancat15_gc0_gm0.ipynb | bpRsh/shear_analysis_after_dmstack |
Plot g00 vs g20 | df.head(2)
def plot_g00_20(df,start,end):
fig,ax = plt.subplots(1,2,figsize=(12,8))
x = df['gm[0]']
y = df['gc[0]']-df['gm[0]']
xx = df['g[0][0]']
yy = df['g[2][0]']-df['g[0][0]']
ax[0].scatter(x,y)
ax[1].scatter(xx,yy)
ax[0].set_ylabel('gc0-gm0')
ax[0].set_xlabel('gm0')
... | (90623, 50)
| Apache-2.0 | Jan_2020/a03_jan13/a01_cleancat15_gc0_gm0.ipynb | bpRsh/shear_analysis_after_dmstack |
AlexNet in TensorFlowCredits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien SetupRefer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md... | import tensorflow as tf
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Parameters
learning_rate = 0.001
training_iters = 300000
batch_size = 64
display_step = 100
# Network Parameters
n_input = 784 # M... | _____no_output_____ | Apache-2.0 | deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb | AadityaGupta/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials |
T81-558: Applications of Deep Neural Networks**Module 6: Convolutional Neural Networks (CNN) for Computer Vision*** Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)* For more i... | # Nicely formatted time string
def hms_string(sec_elapsed):
h = int(sec_elapsed / (60 * 60))
m = int((sec_elapsed % (60 * 60)) / 60)
s = sec_elapsed % 60
return f"{h}:{m:>02}:{s:>05.2f}" | _____no_output_____ | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Part 6.2: Keras Neural Networks for Digits and Fashion MINST Computer VisionThis class will focus on computer vision. There are some important differences and similarities with previous neural networks.* We will usually use classification, though regression is still an option.* The input to the neural network is now ... | import tensorflow.keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras import regularizers
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print("Shape of x_train: {}".format(x_train.shap... | Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
Shape of x_train: (60000, 28, 28)
Shape of y_train: (60000,)
Shape of x_test: (10000, 28, 28)
Shape of y_test: (10000,)
| Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Display the Digits The following code shows what the MNIST files contain. | # Display as text
from IPython.display import display
import pandas as pd
print("Shape for dataset: {}".format(x_train.shape))
print("Labels: {}".format(y_train))
# Single MNIST digit
single = x_train[0]
print("Shape for single: {}".format(single.shape))
display(pd.DataFrame(single.reshape(28,28)))
# Display as imag... | x_train shape: (60000, 28, 28, 1)
Training samples: 60000
Test samples: 10000
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for up... | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Training/Fitting CNN - DIGITSThe following code will train the CNN for 20,000 steps. This can take awhile, you might want to scale the step count back. GPU training can help. My results:* CPU Training Time: Elapsed time: 1:50:13.10* GPU Training Time: Elapsed time: 0:13:43.06 | import tensorflow as tf
import time
start_time = time.time()
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss: {}'.format(score[0]))
print('Test accu... | Train on 60000 samples, validate on 10000 samples
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/12
- 8s ... | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Evaluate Accuracy - DIGITSNote, if you are using a GPU you might get the **ResourceExhaustedError**. This occurs because the GPU might not have enough ram to predict the entire data set at once. | # Predict using either GPU or CPU, send the entire dataset. This might not work on the GPU.
# Set the desired TensorFlow output level for this example
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss: {}'.format(score[0]))
print('Test accuracy: {}'.format(score[1])) | Test loss: 0.027716550575438943
Test accuracy: 0.9919999837875366
| Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
GPUs are most often used for training rather than prediction. For prediction either disable the GPU or just predict on a smaller sample. If your GPU has enough memory, the above prediction code may work just fine. If not, just prediction on a sample with the following code: | from sklearn import metrics
# For GPU just grab the first 100 images
small_x = x_test[1:100]
small_y = y_test[1:100]
small_y2 = np.argmax(small_y,axis=1)
pred = model.predict(small_x)
pred = np.argmax(pred,axis=1)
score = metrics.accuracy_score(small_y2, pred)
print('Accuracy: {}'.format(score)) | Accuracy: 0.98989898989899
| Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
MINST Fashion | import tensorflow.keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras import regularizers
from tensorflow.keras.datasets import fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
print("Shape of x_train: {}".for... | Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [=====================... | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Display the Apparel The following code shows what the Fashion MNIST files contain. | # Display as text
from IPython.display import display
import pandas as pd
print("Shape for dataset: {}".format(x_train.shape))
print("Labels: {}".format(y_train))
# Single MNIST digit
single = x_train[0]
print("Shape for single: {}".format(single.shape))
display(pd.DataFrame(single.reshape(28,28)))
# Display as imag... | _____no_output_____ | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
Training/Fitting CNN - FashionThe following code will train the CNN for 20,000 steps. This can take awhile, you might want to scale the step count back. GPU training can help. My results:* CPU Training Time: Elapsed time: 1:50:13.10* GPU Training Time: Elapsed time: 0:13:43.06 | import tensorflow.keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
... | Train on 60000 samples, validate on 10000 samples
Epoch 1/12
- 4s - loss: 0.5299 - acc: 0.8125 - val_loss: 0.3500 - val_acc: 0.8740
Epoch 2/12
- 4s - loss: 0.3456 - acc: 0.8774 - val_loss: 0.2875 - val_acc: 0.8960
Epoch 3/12
- 4s - loss: 0.2973 - acc: 0.8927 - val_loss: 0.2736 - val_acc: 0.9018
Epoch 4/12
- 4s - lo... | Apache-2.0 | t81_558_class_06_2_cnn.ipynb | sanjayssane/t81_558_deep_learning |
import numpy as np
A = np.array([[4,10,8],[10,26,26],[8,26,61]])
print(A)
inv_A=np.linalg.inv(A)
print(inv_A)
B = np.array([[44],[128],[214]])
print(B)
#AA^-1X = B.A^-1
X = np.dot(inv_A,B)
print(X)
| [[ 4 10 8]
[10 26 26]
[ 8 26 61]]
[[ 25.27777778 -11.16666667 1.44444444]
[-11.16666667 5. -0.66666667]
[ 1.44444444 -0.66666667 0.11111111]]
[[ 44]
[128]
[214]]
[[-8.]
[ 6.]
[ 2.]]
| Apache-2.0 | Linear_Transformation.ipynb | espinili/Linear-Algebra-58020 | |
Load datasets | from sklearn import datasets
digits = datasets.load_digits()
features = digits.data
target = digits.target | _____no_output_____ | MIT | Machine Learning Cookbook/Chapter 2 Loading Data.ipynb | sonwanesuresh95/Books-to-notebooks |
Need 240 metric tons of methane. 2.4e^8 grams of methane | from ISRU import Atmospheric_processing_unit
import random
import json
import matplotlib.pyplot as plt
peridoic = json.load(open("periodic.json"))
avogadro = 6.0221409E23
H = peridoic['H']
C = peridoic['C']
CH_4 = {}
CH_4['mass'] = (1 * C['atomic_mass']) + (4 * H['atomic_mass'])
CH_4['mass']
apu = Atmospheric_process... | _____no_output_____ | MIT | Untitled.ipynb | mkirby1995/PyISRU |
**This notebook is an exercise in the [Python](https://www.kaggle.com/learn/python) course. You can reference the tutorial at [this link](https://www.kaggle.com/colinmorris/hello-python).**--- Exercises Welcome to your first set of Python coding problems! If this is your first time using Kaggle Notebooks, welcome! No... | print("You've successfully run some Python code")
print("Congratulations!")
print("this is sai yugandhar")
| _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
Try adding another line of code in the cell above and re-running it. Now let's get a little fancier: Add a new code cell by clicking on an existing code cell, hitting the escape key, and then hitting the `a` or `b` key. The `a` key will add a cell above the current cell, and `b` adds a cell below.Great! Now you know ... | from learntools.core import binder; binder.bind(globals())
from learntools.python.ex1 import *
print("Setup complete! You're ready to start question 0.") | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
0.*This is a silly question intended as an introduction to the format we use for hands-on exercises throughout all Kaggle courses.***What is your favorite color? **To complete this question, create a variable called `color` in the cell below with an appropriate value. The function call `q0.check()` (which we've alread... | # create a variable called color with an appropriate value on the line below
# (Remember, strings in Python must be enclosed in 'single' or "double" quotes
colour=("blue")
# Check your answer
q0.check() | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
Didn't get the right answer? How do you not even know your own favorite color?!Delete the `` in the line below to make one of the lines run. You can choose between getting a hint or the full answer by choosing which line to remove the `` from. Removing the `` is called uncommenting, because it changes that line from a ... | q0.hint()
q0.solution() | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
The upcoming questions work the same way. The only thing that will change are the question numbers. For the next question, you'll call `q1.check()`, `q1.hint()`, `q1.solution()`, for question 2, you'll call `q2.check()`, and so on. 1.Complete the code below. In case it's helpful, here is the table of available arithme... | pi = 3.14159 # approximate
diameter = 3
# Create a variable called 'radius' equal to half the diameter
radius=1/2*diameter
# Create a variable called 'area', using the formula for the area of a circle: pi times the radius squared
area=pi*radius**2
# Check your answer
q1.check()
# Uncomment and run the lines below if... | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
2.Add code to the following cell to swap variables `a` and `b` (so that `a` refers to the object previously referred to by `b` and vice versa). | ########### Setup code - don't touch this part ######################
# If you're curious, these are examples of lists. We'll talk about
# them in depth a few lessons from now. For now, just know that they're
# yet another type of Python object, like int or float.
a = [1, 2, 3]
b = [3, 2, 1]
q2.store_original_ids()
##... | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
3.a) Add parentheses to the following expression so that it evaluates to 1. | ((5 - 3) // 2)
q3.a.hint()
# Check your answer (Run this code cell to receive credit!)
q3.a.solution() | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
Questions, like this one, marked a spicy pepper are a bit harder.b) 🌶️ Add parentheses to the following expression so that it evaluates to 0 | (8 - 3) * (2 - (1 + 1))
#q3.b.hint()
# Check your answer (Run this code cell to receive credit!)
q3.b.solution() | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
4. Alice, Bob and Carol have agreed to pool their Halloween candy and split it evenly among themselves.For the sake of their friendship, any candies left over will be smashed. For example, if they collectivelybring home 91 candies, they'll take 30 each and smash 1.Write an arithmetic expression below to calculate how ... | # Variables representing the number of candies collected by alice, bob, and carol
alice_candies = 42
bob_candies = 28
carol_candies = 21
# Your code goes here! Replace the right-hand side of this assignment with an expression
# involving alice_candies, bob_candies, and carol_candies
to_smash = 1
sum = alice_candies+bo... | _____no_output_____ | MIT | python/syntax-variables-and-numbers.ipynb | saiyugandharsingamaneni/kaggel-courses |
LMDZ GCM[LMDZ](http://lmdz.lmd.jussieu.fr/le-projet-lmdz/formation/2017)[mars?](http://www-mars.lmd.jussieu.fr/) Download, compile | %%bash
run=svn && which $run || sudo apt-get install subversion || true
which $run || echo "Please install svn..."
INST="svn co http://svn.lmd.jussieu.fr/LMDZ/BOL/script_install && cd script_install && chmod +x install*.sh && ./install*.sh"
echo $INST
######################
from __future__ import print_function
import ... | _____no_output_____ | Apache-2.0 | LMDZ.ipynb | morianemo/ecflow-notehbook2 |
ConvNet for image classification (CIFAR-10) | import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.datasets as dset
import torchvision.transforms as T
from torch.utils.data import DataLoader
from torch.utils.data import sampler
from pprint import ppr... | using device: cuda
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Load CIFAR-10 | # Set up a transform to preprocess the data by subtracting the mean RGB value
# and dividing by the standard deviation of each RGB value;
transform = T.Compose([
T.ToTensor(),
T.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])
# Set up a Dataset object for e... | Files already downloaded and verified
Files already downloaded and verified
Files already downloaded and verified
X_train: (49000, 32, 32, 3), [0, 255]
X_val: (1000, 32, 32, 3), [0, 255]
classes: ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Look at some images | plt.figure(figsize=(8, 4))
batch = (X_train[0:10])
for i in range(10):
plt.subplot(2, 5, i + 1)
plt.imshow(batch[i].astype('int32'))
plt.axis('off')
plt.title(classes[y_train[i]])
plt.tight_layout() | _____no_output_____ | MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
ConvNet | in_channel = 3
channel_1 = 64
channel_2 = 16
num_classes = 10
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.shape[0], -1)
def weights_init(m):
if type(m) in [nn.Conv2d, nn.Linear]:
nn.init.zeros_(m.bias.data)
if INIT_METHOD == 'he_normal':
nn.init.kaim... | _____no_output_____ | MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Training | weights = {}
val_acc = train(model, optimizer, epochs=EPOCHS)
plt.figure()
plt.plot(np.arange(len(val_acc)) + 1, val_acc)
plt.xlabel('epoch')
plt.ylabel('accuracy, %')
plt.title('Validation accuracy')
plt.show() | _____no_output_____ | MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Results | check_accuracy(loader_train, model, dataset='train')
val_acc = check_accuracy(loader_val, model)
test_acc = check_accuracy(loader_test, model) | Checking accuracy on train set
Got 40262 / 49000 correct (82.17%)
Checking accuracy on validation set
Got 691 / 1000 correct (69.10%)
Checking accuracy on test set
Got 6867 / 10000 correct (68.67%)
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Searching the weights of the model :) | print(model)
print(model[0])
print(model[0].weight.shape) | Conv2d(3, 64, kernel_size=(5, 5), stride=(1, 1))
torch.Size([64, 3, 5, 5])
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Final conv1 weights/filters | print(f'PyTorch')
print(f'init_method: {INIT_METHOD}')
print(f'val_acc: {val_acc:.2f}%')
def normalize_img(x):
x_min = x.min()
x_max = x.max()
x_norm = (x - x_min) / (x_max - x_min)
return x_norm
plt.figure(figsize=(22, 7))
for i in range(len(model[0].weight)):
conv1_filter = model[0].weight[i].tr... | PyTorch
init_method: glorot_uniform
val_acc: 69.10%
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Conv1 weights/filter during training | imgs_per_line = 16
k = 0
for epoch in weights.keys():
print(f'epoch {epoch}')
plt.figure(figsize=(18, 12))
for i in range(imgs_per_line):
k += 1
plt.subplot(len(weights), imgs_per_line, k)
w = weights[epoch][i].transpose(0, 1).transpose(1, 2).cpu().detach().numpy()
plt.imsh... | epoch 0
| MIT | 2018-2019/assignment 3 (CNN)/ConvNet_CIFAR10_PyTorch.ipynb | Tudor67/Neural-Networks-Assignments |
Balanced Binning | from yellowbrick.datasets import load_concrete
from yellowbrick.target import BalancedBinningReference
# Instantiate the visualizer
visualizer = BalancedBinningReference(bins=[0,7,8,9,10])
y = df['RATINGHS']
visualizer.fit(y) # Fit the data to the visualizer
visualizer.show() # Finalize and render the ... | _____no_output_____ | MIT | .ipynb_checkpoints/Household_Class_Imbalanced-checkpoint.ipynb | georgetown-analytics/First-Home-Recommender |
Class Imbalanced | X = df
y = df_conv
from yellowbrick.target import ClassBalance
X = df
y = df_conv
# Instantiate the visualizer
visualizer = ClassBalance(
labels=["Un-Satisfied", "Satisfied", "Highly Satisfied","Extreme Satisfied"], size=(1080, 720)
)
visualizer.fit(y)
visualizer.show()
from imblearn.over_sampling import SMOTE
sm ... | /Users/sabashaikh/anaconda2/envs/py36/lib/python3.6/site-packages/imblearn/base.py:306: UserWarning: The target type should be binary.
warnings.warn('The target type should be binary.')
| MIT | .ipynb_checkpoints/Household_Class_Imbalanced-checkpoint.ipynb | georgetown-analytics/First-Home-Recommender |
Вступ Створити програму, яка виконує наступні завдання:1. Створити не менше двох об’єктів TimeSeries, у яких індекси створені задопомогою date_range(). Виділити підмасиви у цих об’єктів. Провестиоб’єднання об’єктів TimeSeries за допомогою merge_asof().2. Виконати завдання відповідно до варіанту. Варіант 7.*Файл Micro... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns | _____no_output_____ | MIT | docs/lab5/lab5.ipynb | mezgoodle/ad_labs |
Дані | df = pd.read_csv('https://raw.githubusercontent.com/mezgoodle/ad_labs/master/data/Microsoft_Stock.csv', index_col='Date', parse_dates=True)
df
df.info()
df.describe().T | _____no_output_____ | MIT | docs/lab5/lab5.ipynb | mezgoodle/ad_labs |
Перше завдання | date_indexes = pd.date_range('2015-04-01 16:00:00', '2021', freq='3D')
date_indexes
open_series = pd.Series(df['Open'], index=date_indexes).fillna(method='ffill')
open_series
date_indexes = pd.date_range('2015-04-01 16:00:00', '2019-11', freq='3B')
date_indexes
close_series = pd.Series(df['Close'], index=date_indexes).... | _____no_output_____ | MIT | docs/lab5/lab5.ipynb | mezgoodle/ad_labs |
Друге завдання | close_series = pd.Series(df['Close'])
close_series.plot()
close_series['2019'].plot()
close_series['2018-09'].plot()
close_series['2015-11': '2018-01'].plot()
close_series['2021-01'].last('2W').plot() | _____no_output_____ | MIT | docs/lab5/lab5.ipynb | mezgoodle/ad_labs |
Третє завдання | high_series = pd.Series(df['High'])
high_series
high_series['2016'].mean()
high_series.to_period('M').groupby(level=0).mean()
high_series['2019'].first('3M').to_period('W').groupby(level=0).mean() | _____no_output_____ | MIT | docs/lab5/lab5.ipynb | mezgoodle/ad_labs |
!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
-O /tmp/horse-or-human.zip
!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \
-O /tmp/validation-horse-or-human.... | --2019-11-15 13:38:59-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 2607:f8b0:400e:c09::80
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.
HTTP ... | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow | |
The following python code will use the OS library to use Operating System libraries, giving you access to the file system, and the zipfile library allowing you to unzip the data. | import os
import zipfile
local_zip = '/tmp/horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/horse-or-human')
local_zip = '/tmp/validation-horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/validation-horse-or-human')
zip_ref.close() | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
The contents of the .zip are extracted to the base directory `/tmp/horse-or-human`, which in turn each contain `horses` and `humans` subdirectories.In short: The training set is the data that is used to tell the neural network model that 'this is what a horse looks like', 'this is what a human looks like' etc. One thin... | # Directory with our training horse pictures
train_horse_dir = os.path.join('/tmp/horse-or-human/horses')
# Directory with our training human pictures
train_human_dir = os.path.join('/tmp/horse-or-human/humans')
# Directory with our training horse pictures
validation_horse_dir = os.path.join('/tmp/validation-horse-or... | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
Building a Small Model from ScratchBut before we continue, let's start defining the model:Step 1 will be to import tensorflow. | import tensorflow as tf | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
We then add convolutional layers as in the previous example, and flatten the final result to feed into the densely connected layers. Finally we add the densely connected layers. Note that because we are facing a two-class classification problem, i.e. a *binary classification problem*, we will end our network with a [*s... | model = tf.keras.models.Sequential([
# Note the input shape is the desired size of the image 150x150 with 3 bytes color
# This is the first convolution
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
# The second convolution
tf... | WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Ker... | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
The model.summary() method call prints a summary of the NN | model.summary() | Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 148, 148, 16) 448
____________________________________... | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
The "output shape" column shows how the size of your feature map evolves in each successive layer. The convolution layers reduce the size of the feature maps by a bit due to padding, and each pooling layer halves the dimensions. Next, we'll configure the specifications for model training. We will train our model with t... | from tensorflow.keras.optimizers import RMSprop
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['acc']) | WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
| Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
Data PreprocessingLet's set up data generators that will read pictures in our source folders, convert them to `float32` tensors, and feed them (with their labels) to our network. We'll have one generator for the training images and one for the validation images. Our generators will yield batches of images of size 300x... | from tensorflow.keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)
# Flow training images in batches of 128 using train_datagen generator
train_generator = train_datagen.fl... | Found 1027 images belonging to 2 classes.
Found 256 images belonging to 2 classes.
| Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
TrainingLet's train for 15 epochs -- this may take a few minutes to run.Do note the values per epoch.The Loss and Accuracy are a great indication of progress of training. It's making a guess as to the classification of the training data, and then measuring it against the known label, calculating the result. Accuracy i... | history = model.fit_generator(
train_generator,
steps_per_epoch=8,
epochs=15,
verbose=1,
validation_data = validation_generator,
validation_steps=8) | Epoch 1/15
6/8 [=====================>........] - ETA: 1s - loss: 2.9195 - acc: 0.4743Epoch 1/15
8/8 [==============================] - 1s 90ms/step - loss: 0.6141 - acc: 0.6953
8/8 [==============================] - 6s 745ms/step - loss: 2.3494 - acc: 0.4950 - val_loss: 0.6141 - val_acc: 0.6953
Epoch 2/15
7/8 [=======... | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
Running the ModelLet's now take a look at actually running a prediction using the model. This code will allow you to choose 1 or more files from your file system, it will then upload them, and run them through the model, giving an indication of whether the object is a horse or a human. | import numpy as np
from google.colab import files
from keras.preprocessing import image
uploaded = files.upload()
for fn in uploaded.keys():
# predicting images
path = '/content/' + fn
img = image.load_img(path, target_size=(150, 150))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = ... | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
Visualizing Intermediate RepresentationsTo get a feel for what kind of features our convnet has learned, one fun thing to do is to visualize how an input gets transformed as it goes through the convnet.Let's pick a random image from the training set, and then generate a figure where each row is the output of a layer, ... | import numpy as np
import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img
# Let's define a new Model that will take an image as input, and will output
# intermediate representations for all layers in the previous model after
# the first.
successive_outputs = [layer.output for layer in mo... | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
As you can see we go from the raw pixels of the images to increasingly abstract and compact representations. The representations downstream start highlighting what the network pays attention to, and they show fewer and fewer features being "activated"; most are set to zero. This is called "sparsity." Representation spa... | import os, signal
os.kill(os.getpid(), signal.SIGKILL) | _____no_output_____ | Apache-2.0 | HorsevsHumanwithLessSize.ipynb | sunneysood/Tensorflow |
Python - Writing Your First Python Code! Welcome! This notebook will teach you the basics of the Python programming language. Although the information presented here is quite basic, it is an important foundation that will help you read and write Python code. By the end of this notebook, you'll know the basics of Pyth... | # Try your first Python output
print('Hello, Python!')
#ini tugas pertama
print ('Nikky Rufiansya')
| Nikky Rufiansya
| MIT | PY0101EN_1_1_Types.ipynb | NikkiRufiansya/ML-Courses |
After executing the cell above, you should see that Python prints Hello, Python!. Congratulations on running your first Python code! [Tip:] print() is a function. You passed the string 'Hello, Python!' as an argument to instruct Python on what to print. What version of Python are we using? There are two popula... | # Check the Python Version
import sys
print(sys.version) | 3.5.2 (default, Oct 8 2019, 13:06:37)
[GCC 5.4.0 20160609]
| MIT | PY0101EN_1_1_Types.ipynb | NikkiRufiansya/ML-Courses |
[Tip:] sys is a built-in module that contains many system-specific parameters and functions, including the Python version in use. Before using it, we must explictly import it. Writing comments in Python In addition to writing code, note that it's always a good idea to add comments to your code. It will help ot... | # Practice on writing comments
print('Hello, Python!') # This line prints a string
# print('Hi')
print('\nHia') | Hello, Python!
Hia
| MIT | PY0101EN_1_1_Types.ipynb | NikkiRufiansya/ML-Courses |
After executing the cell above, you should notice that This line prints a string did not appear in the output, because it was a comment (and thus ignored by Python). The second line was also not executed because print('Hi') was preceded by the number sign () as well! Since this isn't an explanatory comment from ... | # Print string as error message
print("Hello, Python!") | Hello, Python!
| MIT | PY0101EN_1_1_Types.ipynb | NikkiRufiansya/ML-Courses |
The error message tells you: where the error occurred (more useful in large notebook cells or scripts), and what kind of error it was (NameError) Here, Python attempted to run the function frint, but could not determine what frint is since it's not a built-in function and it has not been previously defined by u... | # Try to see build in error message
print("Hello, Python!") | Hello, Python!
| MIT | PY0101EN_1_1_Types.ipynb | NikkiRufiansya/ML-Courses |
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