markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
Install TFX | !pip install -U tfx | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Did you restart the runtime?
If you are using Google Colab, the first time that you run
the cell above, you must restart the runtime by clicking
above "RESTART RUNTIME" button or using "Runtime > Restart
runtime ..." menu. This is because of the way that Colab
loads packages.
Check the TensorFlow and TFX versions. | import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__)) | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Set up variables
There are some variables used to define a pipeline. You can customize these
variables as you want. By default all output from the pipeline will be
generated under the current directory. | import os
PIPELINE_NAME = "penguin-simple"
# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where c... | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Prepare example data
We will download the example dataset for use in our TFX pipeline. The dataset we
are using is
Palmer Penguins dataset
which is also used in other
TFX examples.
There are four numeric features in this dataset:
culmen_length_mm
culmen_depth_mm
flipper_length_mm
body_mass_g
All features were already... | import urllib.request
import tempfile
DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlr... | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Take a quick look at the CSV file. | !head {_data_filepath} | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
You should be able to see five values. species is one of 0, 1 or 2, and all
other features should have values between 0 and 1.
Create a pipeline
TFX pipelines are defined using Python APIs. We will define a pipeline which
consists of following three components.
- CsvExampleGen: Reads in data files and convert them to T... | _trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_met... | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Now you have completed all preparation steps to build a TFX pipeline.
Write a pipeline definition
We define a function to create a TFX pipeline. A Pipeline object
represents a TFX pipeline which can be run using one of the pipeline
orchestration systems that TFX supports. | def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
module_file: str, serving_model_dir: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a three component penguin pipeline with TFX."""
# Brings data into the pipeline.
example_gen = tfx.co... | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Run the pipeline
TFX supports multiple orchestrators to run pipelines.
In this tutorial we will use LocalDagRunner which is included in the TFX
Python package and runs pipelines on local environment.
We often call TFX pipelines "DAGs" which stands for directed acyclic graph.
LocalDagRunner provides fast iterations for ... | tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
module_file=_trainer_module_file,
serving_model_dir=SERVING_MODEL_DIR,
metadata_path=METADATA_PATH)) | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
You should see "INFO:absl:Component Pusher is finished." at the end of the
logs if the pipeline finished successfully. Because Pusher component is the
last component of the pipeline.
The pusher component pushes the trained model to the SERVING_MODEL_DIR which
is the serving_model/penguin-simple directory if you did not... | # List files in created model directory.
!find {SERVING_MODEL_DIR} | site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb | tensorflow/docs-l10n | apache-2.0 |
Source localization with MNE/dSPM/sLORETA/eLORETA
The aim of this tutorial is to teach you how to compute and apply a linear
inverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data. | # sphinx_gallery_thumbnail_number = 10
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Process MEG data | data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname) # already has an average reference
events = mne.find_events(raw, stim_channel='STI 014')
event_id = dict(aud_l=1) # event trigger and conditions
tmin = -0.2 # start of each epoc... | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Compute regularized noise covariance
For more details see tut_compute_covariance. | noise_cov = mne.compute_covariance(
epochs, tmax=0., method=['shrunk', 'empirical'], rank=None, verbose=True)
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info) | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Compute the evoked response
Let's just use MEG channels for simplicity. | evoked = epochs.average().pick_types(meg=True)
evoked.plot(time_unit='s')
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',
time_unit='s')
# Show whitening
evoked.plot_white(noise_cov, time_unit='s')
del epochs # to save memory | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Inverse modeling: MNE/dSPM on evoked and raw data | # Read the forward solution and compute the inverse operator
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fname_fwd)
# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
... | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Compute inverse solution | method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc, residual = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori=None,
return_residual=True, verbose=True) | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Visualization
View activation time-series | plt.figure()
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show() | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Examine the original data and the residual after fitting: | fig, axes = plt.subplots(2, 1)
evoked.plot(axes=axes)
for ax in axes:
ax.texts = []
for line in ax.lines:
line.set_color('#98df81')
residual.plot(axes=axes) | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Here we use peak getter to move visualization to the time point of the peak
and draw a marker at the maximum peak vertex. | vertno_max, time_max = stc.get_peak(hemi='rh')
subjects_dir = data_path + '/subjects'
surfer_kwargs = dict(
hemi='rh', subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_k... | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Morph data to average brain | # setup source morph
morph = mne.compute_source_morph(
src=inverse_operator['src'], subject_from=stc.subject,
subject_to='fsaverage', spacing=5, # to ico-5
subjects_dir=subjects_dir)
# morph data
stc_fsaverage = morph.apply(stc)
brain = stc_fsaverage.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Morphed... | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Dipole orientations
The pick_ori parameter of the
:func:mne.minimum_norm.apply_inverse function controls
the orientation of the dipoles. One useful setting is pick_ori='vector',
which will return an estimate that does not only contain the source power at
each dipole, but also the orientation of the dipoles. | stc_vec = apply_inverse(evoked, inverse_operator, lambda2,
method=method, pick_ori='vector')
brain = stc_vec.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)
del stc_vec | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Note that there is a relationship between the orientation of the dipoles and
the surface of the cortex. For this reason, we do not use an inflated
cortical surface for visualization, but the original surface used to define
the source space.
For more information about dipole orientations, see
sphx_glr_auto_tutorials_plo... | for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]),
('sLORETA', [3, 5, 7]),
('eLORETA', [0.75, 1.25, 1.75]),)):
surfer_kwargs['clim']['lims'] = lims
stc = apply_inverse(evoked, inverse_operator, lambda2,
me... | 0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
There are some infeasibilities without line extensions. | for line_name in ["316", "527", "602"]:
network.lines.loc[line_name, "s_nom"] = 1200
now = network.snapshots[0] | examples/notebooks/scigrid-sclopf.ipynb | PyPSA/PyPSA | mit |
Performing security-constrained linear OPF | branch_outages = network.lines.index[:15]
network.sclopf(now, branch_outages=branch_outages, solver_name="cbc") | examples/notebooks/scigrid-sclopf.ipynb | PyPSA/PyPSA | mit |
For the PF, set the P to the optimised P. | network.generators_t.p_set = network.generators_t.p_set.reindex(
columns=network.generators.index
)
network.generators_t.p_set.loc[now] = network.generators_t.p.loc[now]
network.storage_units_t.p_set = network.storage_units_t.p_set.reindex(
columns=network.storage_units.index
)
network.storage_units_t.p_set.lo... | examples/notebooks/scigrid-sclopf.ipynb | PyPSA/PyPSA | mit |
Check no lines are overloaded with the linear contingency analysis | p0_test = network.lpf_contingency(now, branch_outages=branch_outages)
p0_test | examples/notebooks/scigrid-sclopf.ipynb | PyPSA/PyPSA | mit |
Check loading as per unit of s_nom in each contingency | max_loading = (
abs(p0_test.divide(network.passive_branches().s_nom, axis=0)).describe().loc["max"]
)
max_loading
np.allclose(max_loading, np.ones((len(max_loading)))) | examples/notebooks/scigrid-sclopf.ipynb | PyPSA/PyPSA | mit |
1.2 Train Ridge Regression on training data
The first step is to train the ridge regression model on the training data with a 5-fold cross-validation with an internal line-search to find the optimal hyperparameter $\alpha$. We will plot the training errors against the validation errors, to illustrate the effect of diff... | #Initialize different alpha values for the Ridge Regression model
alphas = sp.logspace(-2,8,11)
param_grid = dict(alpha=alphas)
#5-fold cross-validation (outer-loop)
outer_cv = KFold(n_splits=5,shuffle=True,random_state=random_state)
#Line-search to find the optimal alpha value (internal-loop)
#Model performance is m... | Toy-Example-Solution.ipynb | dominikgrimm/ridge_and_svm | mit |
1.3 Train Ridge Regression with optimal $\alpha$ and evaluate model in test data
Next we retrain the ridge regresssion model with the optimal $\alpha$ (from the last section). After re-training we will test the model on the not used test data to evaluate the model performance on unseen data. | #Train Ridge Regression on the full training data with optimal alpha
model = Ridge(alpha=optimal_alpha,solver="cholesky")
model.fit(training_data,training_target)
#Use trained model the predict new instances in test data
predictions = model.predict(testing_data)
print("Prediction results on test data")
print("MSE (tes... | Toy-Example-Solution.ipynb | dominikgrimm/ridge_and_svm | mit |
<div style="text-align:justify">
Using 5-fold cross-validation on the training data leads to a mean squared error (MSE) of $MSE=587.09 \pm 53.54$. On the test data we get an error of $MSE=699.56$ ($\sim 26.5$ days). That indicates that the ridge regression model performs rather mediocre (even with hyperparameter op... | #Split data into training and testing splits, stratified by class-ratios
stratiefied_splitter = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)
for train_index,test_index in stratiefied_splitter.split(data,binary_target):
training_data = data[train_index,:]
training_target = binary_target[train... | Toy-Example-Solution.ipynb | dominikgrimm/ridge_and_svm | mit |
2.2 Classification with a linear SVM | Cs = sp.logspace(-7, 1, 9)
param_grid = dict(C=Cs)
grid = GridSearchCV(SVC(kernel="linear",random_state=random_state),
param_grid=param_grid,
scoring="accuracy",
n_jobs=4,
return_train_score=True)
outer_cv = StratifiedKFold(n_splits=5,shuf... | Toy-Example-Solution.ipynb | dominikgrimm/ridge_and_svm | mit |
2.3 Classification with SVM and RBF kernel | Cs = sp.logspace(-4, 4, 9)
gammas = sp.logspace(-7, 1, 9)
param_grid = dict(C=Cs,gamma=gammas)
grid = GridSearchCV(SVC(kernel="rbf",random_state=42),
param_grid=param_grid,
scoring="accuracy",
n_jobs=4,
return_train_score=True)
outer_cv =... | Toy-Example-Solution.ipynb | dominikgrimm/ridge_and_svm | mit |
We use the <b> Iris dataset </b>
https://en.m.wikipedia.org/wiki/Iris_flower_data_set
The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres... | model = LogisticRegression()
model.fit(dataset.data, dataset.target)
expected = dataset.target
predicted = model.predict(dataset.data)
# classification metrics report builds a text report showing the main classification metrics
# In pattern recognition and information retrieval with binary classification,
# precisio... | Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb | catalystcomputing/DSIoT-Python-sessions | apache-2.0 |
We typically need the following libraries:
<b> NumPy </b> Numerical Python - mainly used for n-dimensional array(which is absent in traditional Python).
Also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fo... | integers_list = [1,3,5,7,9] # lists are seperated by square brackets
print(integers_list)
tuple_integers = 1,3,5,7,9 #tuples are seperated by commas and are immutable
print(tuple_integers)
tuple_integers[0] = 11
#Python strings can be in single or double quotes
string_ds = "Data Science"
string_iot = "Internet of Thi... | Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb | catalystcomputing/DSIoT-Python-sessions | apache-2.0 |
Evaluation of your model | # let's try to visualize the estimated and real house values for all data points in test dataset
fig, ax = plt.subplots(figsize=(15, 5))
plt.subplot(1, 2, 1)
plt.plot(X_test_1,predictions_1, 'o')
plt.xlabel('% of lower status of the population')
plt.ylabel('Estimated home value in $1000s')
plt.subplot(1, 2, 2)
plt... | aps/notebooks/ml_varsom/linear_regression.ipynb | kmunve/APS | mit |
To evaulate the performance of the model, we can compute the error between the real house value (y_test_1) and the predicted values we got form our model (predictions_1).
One such metric is called the residual sum of squares (RSS): | # first we define our RSS function
def RSS(y, p):
return sum((y - p)**2)
# then we calculate RSS:
RSS_model_1 = RSS(y_test_1, predictions_1)
RSS_model_1 | aps/notebooks/ml_varsom/linear_regression.ipynb | kmunve/APS | mit |
This number doesn't tell us much - is 7027 good? Is it bad?
Unfortunatelly, there is no right answer - it depends on the data. Sometimes RSS of 7000 indicates very bad model, and sometimes 7000 is as good as it gets.
That's why we use RSS when comparing models - the model with lowest RSS is the best.
The other metri... | lm1.score(X_test_1,y_test_1) | aps/notebooks/ml_varsom/linear_regression.ipynb | kmunve/APS | mit |
This means that only 51% of variability is explained by our model.
In general, $R^{2}$ is a number between 0 and 1 - the closer it is to 1, the better the model is.
Since we got only 0.51, we can conclude that this is not a very good model.
But we can try to build a model with second variable - RM - and check if we ... | # we just repeat everything as before
X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(boston_data[['RM']], boston_data.MEDV,
random_state = 222, test_size = 0.3) # split the data
lm = linear_model.LinearRegression()
model_2 = lm.fit(X_train_2, ... | aps/notebooks/ml_varsom/linear_regression.ipynb | kmunve/APS | mit |
Since RSS is lower for second modell (and lower the RSS, better the model) and $R^{2}$ is higher for second modell (and we want $R^{2}$ as close to 1 as possible), both measures tells us that second model is better.
However, difference is not big - out second model performs slightly better, but we still can't say it fi... | X = boston_data[['CRIM', 'ZN', 'INDUS', 'CHAS', 'RM', 'AGE', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']]
y = boston_data["MEDV"]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 222, test_size = 0.3) # split the data
lm = linear_model.LinearRegression()
model_lr = lm.fit(X_train, y_train) # train ... | aps/notebooks/ml_varsom/linear_regression.ipynb | kmunve/APS | mit |
Navigation
Handling the loop yourself
Comparing with the high level API
1. Handling the loop yourself
For the purposes of this notebook we are going to use one of the predefined objective functions that come with GPyOpt. However, the key thing to realize is that the function could be anything (e.g., the results o... | from emukit.test_functions import forrester_function
from emukit.core.loop import UserFunctionWrapper
target_function, space = forrester_function() | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
First we are going to run the optimization loop outside of Emukit, and only use the library to get the next point at which to evaluate our function.
There are two things to pay attention to when creating the main optimization object:
Since we recreate the object anew for each iteration, we need to pass data about all... | X = np.array([[0.1],[0.6],[0.9]])
Y = target_function(X) | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
And we run the loop externally. | from emukit.examples.gp_bayesian_optimization.single_objective_bayesian_optimization import GPBayesianOptimization
from emukit.core.loop import UserFunctionResult
num_iterations = 10
bo = GPBayesianOptimization(variables_list=space.parameters, X=X, Y=Y)
results = None
for _ in range(num_iterations):
X_new = bo.g... | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
Let's visualize the results. The size of the marker denotes the order in which the point was evaluated - the bigger the marker the later was the evaluation. | x = np.arange(0.0, 1.0, 0.01)
y = target_function(x)
plt.figure()
plt.plot(x, y)
for i, (xs, ys) in enumerate(zip(X, Y)):
plt.plot(xs, ys, 'ro', markersize=10 + 10 * (i+1)/len(X))
X | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
2. Comparing with the high level API
To compare the results, let's now execute the whole loop with Emukit. | X = np.array([[0.1],[0.6],[0.9]])
Y = target_function(X)
bo_loop = GPBayesianOptimization(variables_list=space.parameters, X=X, Y=Y)
bo_loop.run_optimization(target_function, num_iterations) | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
Now let's print the results of this optimization and compare it to the previous external evaluation run. As before, the size of the marker corresponds to its evaluation order. | x = np.arange(0.0, 1.0, 0.01)
y = target_function(x)
plt.figure()
plt.plot(x, y)
for i, (xs, ys) in enumerate(zip(bo_loop.model.model.X, bo_loop.model.model.Y)):
plt.plot(xs, ys, 'ro', markersize=10 + 10 * (i+1)/len(bo_loop.model.model.X)) | notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb | EmuKit/emukit | apache-2.0 |
Test data
We create test data consisting of 5 variables. | psi0 = np.array([
[ 0. , 0. , -0.25, 0. , 0. ],
[-0.38, 0. , 0.14, 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0.44, -0.2 , -0.09, 0. , 0. ],
[ 0.07, -0.06, 0. , 0.07, 0. ]
])
phi1 = np.array([
[-0.04, -0.29, -0.26, 0.14, 0.47],
[-0.42, 0.2 , 0.1 , 0.24, 0.... | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Causal Discovery
To run causal discovery, we create a VARMALiNGAM object and call the fit method. | model = lingam.VARMALiNGAM(order=(1, 1), criterion=None)
model.fit(X) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Using the causal_order_ properties, we can see the causal ordering as a result of the causal discovery. | model.causal_order_ | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Also, using the adjacency_matrices_ properties, we can see the adjacency matrix as a result of the causal discovery. | # psi0
model.adjacency_matrices_[0][0]
# psi1
model.adjacency_matrices_[0][1]
# omega0
model.adjacency_matrices_[1][0] | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Using DirectLiNGAM for the residuals_ properties, we can calculate psi0 matrix. | dlingam = lingam.DirectLiNGAM()
dlingam.fit(model.residuals_)
dlingam.adjacency_matrix_ | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
We can draw a causal graph by utility funciton | labels = ['y0(t)', 'y1(t)', 'y2(t)', 'y3(t)', 'y4(t)', 'y0(t-1)', 'y1(t-1)', 'y2(t-1)', 'y3(t-1)', 'y4(t-1)']
make_dot(np.hstack(model.adjacency_matrices_[0]), lower_limit=0.3, ignore_shape=True, labels=labels) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Independence between error variables
To check if the LiNGAM assumption is broken, we can get p-values of independence between error variables. The value in the i-th row and j-th column of the obtained matrix shows the p-value of the independence of the error variables $e_i$ and $e_j$. | p_values = model.get_error_independence_p_values()
print(p_values) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Bootstrap
Bootstrapping
We call bootstrap() method instead of fit(). Here, the second argument specifies the number of bootstrap sampling. | model = lingam.VARMALiNGAM()
result = model.bootstrap(X, n_sampling=100) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Causal Directions
Since BootstrapResult object is returned, we can get the ranking of the causal directions extracted by get_causal_direction_counts() method. In the following sample code, n_directions option is limited to the causal directions of the top 8 rankings, and min_causal_effect option is limited to causal di... | cdc = result.get_causal_direction_counts(n_directions=8, min_causal_effect=0.4, split_by_causal_effect_sign=True) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
We can check the result by utility function. | labels = ['y0(t)', 'y1(t)', 'y2(t)', 'y3(t)', 'y4(t)', 'y0(t-1)', 'y1(t-1)', 'y2(t-1)', 'y3(t-1)', 'y4(t-1)', 'e0(t-1)', 'e1(t-1)', 'e2(t-1)', 'e3(t-1)', 'e4(t-1)']
print_causal_directions(cdc, 100, labels=labels) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Directed Acyclic Graphs
Also, using the get_directed_acyclic_graph_counts() method, we can get the ranking of the DAGs extracted. In the following sample code, n_dags option is limited to the dags of the top 3 rankings, and min_causal_effect option is limited to causal directions with a coefficient of 0.3 or more. | dagc = result.get_directed_acyclic_graph_counts(n_dags=3, min_causal_effect=0.3, split_by_causal_effect_sign=True) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
We can check the result by utility function. | print_dagc(dagc, 100, labels=labels) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Probability
Using the get_probabilities() method, we can get the probability of bootstrapping. | prob = result.get_probabilities(min_causal_effect=0.1)
print('Probability of psi0:\n', prob[0])
print('Probability of psi1:\n', prob[1])
print('Probability of omega1:\n', prob[2]) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Total Causal Effects
Using the get_total causal_effects() method, we can get the list of total causal effect. The total causal effects we can get are dictionary type variable.
We can display the list nicely by assigning it to pandas.DataFrame. Also, we have replaced the variable index with a label below. | causal_effects = result.get_total_causal_effects(min_causal_effect=0.01)
df = pd.DataFrame(causal_effects)
df['from'] = df['from'].apply(lambda x : labels[x])
df['to'] = df['to'].apply(lambda x : labels[x])
df | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
We can easily perform sorting operations with pandas.DataFrame. | df.sort_values('effect', ascending=False).head() | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
And with pandas.DataFrame, we can easily filter by keywords. The following code extracts the causal direction towards y2(t). | df[df['to']=='y2(t)'].head() | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
Because it holds the raw data of the causal effect (the original data for calculating the median), it is possible to draw a histogram of the values of the causal effect, as shown below. | import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline
from_index = 5 # index of y0(t-1). (index:0)+(n_features:5)*(lag:1) = 5
to_index = 2 # index of y2(t). (index:2)+(n_features:5)*(lag:0) = 2
plt.hist(result.total_effects_[:, to_index, from_index]) | examples/VARMALiNGAM.ipynb | cdt15/lingam | mit |
First we'll load the text file and convert it into integers for our network to use. | with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c in text], dtype=np.int32)
text[:100]
chars[:100] | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
Now I need to split up the data into batches, and into training and validation sets. I should be making a test set here, but I'm not going to worry about that. My test will be if the network can generate new text.
Here I'll make both input and target arrays. The targets are the same as the inputs, except shifted one ch... | def split_data(chars, batch_size, num_steps, split_frac=0.9):
"""
Split character data into training and validation sets, inputs and targets for each set.
Arguments
---------
chars: character array
batch_size: Size of examples in each of batch
num_steps: Number of sequence steps to kee... | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
I'll write another function to grab batches out of the arrays made by split data. Here each batch will be a sliding window on these arrays with size batch_size X num_steps. For example, if we want our network to train on a sequence of 100 characters, num_steps = 100. For the next batch, we'll shift this window the next... | def get_batch(arrs, num_steps):
batch_size, slice_size = arrs[0].shape
n_batches = int(slice_size/num_steps)
for b in range(n_batches):
yield [x[:, b*num_steps: (b+1)*num_steps] for x in arrs]
def build_rnn(num_classes, batch_size=50, num_steps=50, lstm_size=128, num_layers=2,
le... | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
Hyperparameters
Here I'm defining the hyperparameters for the network. The two you probably haven't seen before are lstm_size and num_layers. These set the number of hidden units in the LSTM layers and the number of LSTM layers, respectively. Of course, making these bigger will improve the network's performance but you... | batch_size = 100
num_steps = 100
lstm_size = 512
num_layers = 2
learning_rate = 0.001 | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
Write out the graph for TensorBoard | model = build_rnn(len(vocab),
batch_size=batch_size,
num_steps=num_steps,
learning_rate=learning_rate,
lstm_size=lstm_size,
num_layers=num_layers)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
... | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
Training
Time for training which is is pretty straightforward. Here I pass in some data, and get an LSTM state back. Then I pass that state back in to the network so the next batch can continue the state from the previous batch. And every so often (set by save_every_n) I calculate the validation loss and save a checkpo... | !mkdir -p checkpoints/anna
epochs = 1
save_every_n = 200
train_x, train_y, val_x, val_y = split_data(chars, batch_size, num_steps)
model = build_rnn(len(vocab),
batch_size=batch_size,
num_steps=num_steps,
learning_rate=learning_rate,
lstm_size=l... | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
Sampling
Now that the network is trained, we'll can use it to generate new text. The idea is that we pass in a character, then the network will predict the next character. We can use the new one, to predict the next one. And we keep doing this to generate all new text. I also included some functionality to prime the ne... | def pick_top_n(preds, vocab_size, top_n=5):
p = np.squeeze(preds)
p[np.argsort(p)[:-top_n]] = 0
p = p / np.sum(p)
c = np.random.choice(vocab_size, 1, p=p)[0]
return c
def sample(checkpoint, n_samples, lstm_size, vocab_size, prime="The "):
prime = "Far"
samples = [c for c in prime]
model... | tensorboard/Anna_KaRNNa.ipynb | mdiaz236/DeepLearningFoundations | mit |
int16 アクティベーションによるトレーニング後の整数量子化
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https://www.tensorflow.org/lite/performance/post_training_integer_quant_16x8"><img src="https://www.tensorflow.org/images/tf_logo_32px.png">View on TensorFlow.org</a> </td>
<td> <a target="_blan... | import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
16x8 量子化モードが使用可能であることを確認します | tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
モデルをトレーニングしてエクスポートする | # Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
# Define the model architecture
model = keras.Seq... | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
この例では、モデルを 1 エポックでトレーニングしたので、トレーニングの精度は 96％ 以下になります。
TensorFlow Lite モデルに変換する
Python TFLiteConverter を使用して、トレーニング済みモデルを TensorFlow Lite モデルに変換できるようになりました。
次に、TFliteConverterを使用してモデルをデフォルトの float32 形式に変換します。 | converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert() | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
.tfliteファイルに書き込みます。 | tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model) | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
モデルを 16x8 量子化モードに量子化するには、最初にoptimizationsフラグを設定してデフォルトの最適化を使用します。次に、16x8 量子化モードがターゲット仕様でサポートされる必要な演算であることを指定します。 | converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8] | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
int8 トレーニング後の量子化の場合と同様に、コンバーターオプションinference_input(output)_typeを tf.int16 に設定することで、完全整数量子化モデルを生成できます。
キャリブレーションデータを設定します。 | mnist_train, _ = tf.keras.datasets.mnist.load_data()
images = tf.cast(mnist_train[0], tf.float32) / 255.0
mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)
def representative_data_gen():
for input_value in mnist_ds.take(100):
# Model has only one input so each data point has one element.
yield ... | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
最後に、通常どおりにモデルを変換します。デフォルトでは、変換されたモデルは呼び出しの便宜上、浮動小数点の入力と出力を引き続き使用します。 | tflite_16x8_model = converter.convert()
tflite_model_16x8_file = tflite_models_dir/"mnist_model_quant_16x8.tflite"
tflite_model_16x8_file.write_bytes(tflite_16x8_model) | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
生成されるファイルのサイズが約1/3であることに注目してください。 | !ls -lh {tflite_models_dir} | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
TensorFlow Lite モデルを実行する
Python TensorFlow Lite インタープリタを使用して TensorFlow Lite モデルを実行します。
モデルをインタープリタに読み込む | interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
interpreter_16x8 = tf.lite.Interpreter(model_path=str(tflite_model_16x8_file))
interpreter_16x8.allocate_tensors() | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
1 つの画像でモデルをテストする | test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
interpreter.set_tensor(input_index, test_image)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
import m... | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
モデルを評価する | # A helper function to evaluate the TF Lite model using "test" dataset.
def evaluate_model(interpreter):
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
# Run predictions on every image in the "test" dataset.
prediction_digits = []
for tes... | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
16x8 量子化モデルで評価を繰り返します。 | # NOTE: This quantization mode is an experimental post-training mode,
# it does not have any optimized kernels implementations or
# specialized machine learning hardware accelerators. Therefore,
# it could be slower than the float interpreter.
print(evaluate_model(interpreter_16x8)) | site/ja/lite/performance/post_training_integer_quant_16x8.ipynb | tensorflow/docs-l10n | apache-2.0 |
Vertex SDK: Train & deploy a TensorFlow model with hosted runtimes (aka pre-built containers)
Installation
Install the latest (preview) version of Vertex SDK. | ! pip3 install -U google-cloud-aiplatform --user | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Restart the Kernel
Once you've installed the Vertex SDK and Google cloud-storage, you need to restart the notebook kernel so it can find the packages. | import os
if not os.getenv("AUTORUN"):
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Before you begin
GPU run-time
Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select Runtime > Change Runtime Type > GPU
Set up your GCP project
The following steps are required, regardless of your notebook environment.
Select or create a GCP project. When you first create a... | 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:"... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend when possible, to choose the region closest to you.
Americas: us-central1
Europe: europe-west4
Asia Pacific: asia-east1
You cannot use a Multi-Reg... | REGION = "us-central1" # @param {type: "string"} | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Authenticate your GCP account
If you are using Google Cloud Notebooks, your environment is already
authenticated. Skip this step.
Note: If you are on an Vertex notebook and run the cell, the cell knows to skip executing the authentication steps. | import os
import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your Google Cloud account. This provides access
# to your Cloud Storage bucket and lets you submit training jobs and prediction
# requests.
# If on Vertex, then don't execute this code
if not ... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Set up variables
Next, set up some variables used throughout the tutorial.
Import libraries and define constants
Import Vertex SDK
Import the Vertex SDK into our Python environment. | import time
from google.cloud.aiplatform import gapic as aip
from google.protobuf import json_format
from google.protobuf.json_format import MessageToJson, ParseDict
from google.protobuf.struct_pb2 import Value | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Vertex AI constants
Setup up the following constants for Vertex AI:
API_ENDPOINT: The Vertex AI API service endpoint for dataset, model, job, pipeline and endpoint services.
API_PREDICT_ENDPOINT: The Vertex AI API service endpoint for prediction.
PARENT: The Vertex AI location root path for dataset, model and endpoint... | # API Endpoint
API_ENDPOINT = "{}-aiplatform.googleapis.com".format(REGION)
# Vertex AI location root path for your dataset, model and endpoint resources
PARENT = "projects/" + PROJECT_ID + "/locations/" + REGION | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Clients
The Vertex SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (Vertex).
You will use several clients in this tutorial, so set them all up upfront.
Dataset Service for managed datasets.
Model Service for manage... | # client options same for all services
client_options = {"api_endpoint": API_ENDPOINT}
def create_model_client():
client = aip.ModelServiceClient(client_options=client_options)
return client
def create_endpoint_client():
client = aip.EndpointServiceClient(client_options=client_options)
return client... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Prepare a trainer script
Package assembly | ! rm -rf cifar
! mkdir cifar
! touch cifar/README.md
setup_cfg = "[egg_info]\n\
tag_build =\n\
tag_date = 0"
! echo "$setup_cfg" > cifar/setup.cfg
setup_py = "import setuptools\n\
# Requires TensorFlow Datasets\n\
setuptools.setup(\n\
install_requires=[\n\
'tensorflow_datasets==1.3.0',\n\
],\n\
pa... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Task.py contents | %%writefile cifar/trainer/task.py
import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow.python.client import device_lib
import argparse
import os
import sys
tfds.disable_progress_bar()
parser = argparse.ArgumentParser()
parser.add_argument('--model-dir', dest='model_dir',
defa... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Store training script on your Cloud Storage bucket | ! rm -f cifar.tar cifar.tar.gz
! tar cvf cifar.tar cifar
! gzip cifar.tar
! gsutil cp cifar.tar.gz gs://$BUCKET_NAME/trainer_cifar.tar.gz | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Train a model
projects.locations.customJobs.create
Request | JOB_NAME = "custom_job_TF_" + TIMESTAMP
TRAIN_IMAGE = "gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest"
TRAIN_NGPU = 1
TRAIN_GPU = aip.AcceleratorType.NVIDIA_TESLA_K80
worker_pool_specs = [
{
"replica_count": 1,
"machine_spec": {
"machine_type": "n1-standard-4",
"acceler... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Example output:
{
"parent": "projects/migration-ucaip-training/locations/us-central1",
"customJob": {
"displayName": "custom_job_TF_20210227173057",
"jobSpec": {
"workerPoolSpecs": [
{
"machineSpec": {
"machineType": "n1-standard-4",
"acceleratorType": "NVIDIA... | request = clients["job"].create_custom_job(parent=PARENT, custom_job=training_job) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Example output:
{
"name": "projects/116273516712/locations/us-central1/customJobs/2970106362064797696",
"displayName": "custom_job_TF_20210227173057",
"jobSpec": {
"workerPoolSpecs": [
{
"machineSpec": {
"machineType": "n1-standard-4",
"acceleratorType": "NVIDIA_TESLA_K80",
... | # The full unique ID for the custom training job
custom_training_id = request.name
# The short numeric ID for the custom training job
custom_training_short_id = custom_training_id.split("/")[-1]
print(custom_training_id) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
projects.locations.customJobs.get
Call | request = clients["job"].get_custom_job(name=custom_training_id) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Example output:
{
"name": "projects/116273516712/locations/us-central1/customJobs/2970106362064797696",
"displayName": "custom_job_TF_20210227173057",
"jobSpec": {
"workerPoolSpecs": [
{
"machineSpec": {
"machineType": "n1-standard-4",
"acceleratorType": "NVIDIA_TESLA_K80",
... | while True:
response = clients["job"].get_custom_job(name=custom_training_id)
if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:
print("Training job has not completed:", response.state)
if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:
break
else:
... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Deploy the model
Load the saved model | import tensorflow as tf
model = tf.keras.models.load_model(model_artifact_dir) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Serving function for image data | CONCRETE_INPUT = "numpy_inputs"
def _preprocess(bytes_input):
decoded = tf.io.decode_jpeg(bytes_input, channels=3)
decoded = tf.image.convert_image_dtype(decoded, tf.float32)
resized = tf.image.resize(decoded, size=(32, 32))
rescale = tf.cast(resized / 255.0, tf.float32)
return rescale
@tf.funct... | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Get the serving function signature | loaded = tf.saved_model.load(model_artifact_dir)
input_name = list(
loaded.signatures["serving_default"].structured_input_signature[1].keys()
)[0]
print("Serving function input:", input_name) | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.