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25 Aug: August 26th is National Dog Day!
Sunday, August 26th is National Dog Day! It's a day to celebrate our canine friends and family – perhaps a little more than we celebrate them every day.
Labor Day is around the corner, a time to reflect and be thankful for the efforts of the working population.


Each is a 30 x 10 inch stretched canvas with a 7/8 inch thick wooden frame that the canvas stretches over. It is a mixed media piece consisting of 3D fabric sculpture, gesso, and acrylic paint. Completed in December 2011.
These unique paintings are from the Study of Fabric collection. Each is a column of loosely twisted and draped fabric creating two very similar yet very different and unique pieces. They are meant to be displayed together just as columns in a building would appear. The loose twists create unique shapes and shadows that are very different in each part of the piece. Each canvas goes together as two parts to create a whole.


text:
The effect of the social security earnings test on male labor supply: New evidence from survey and administrative data. (Haider & Loughran 2008)
Earnings and wages-Low-Mixed impacts
Earnings and wages
Employer benefits receipt-Low-Favorable impacts
Employer benefits receipt
Haider, S. J., & Loughran, D. S. (2008). The effect of the social security earnings test on male labor supply: New evidence from survey and administrative data. Journal of Human Resources, 43(1), 57-87.
The study's objective is to examine the impact of 1983 and 2000 policy changes that removed the Social Security earnings test for certain age groups on the earnings and employment outcomes of affected men
The study is a nonexperimental analysis that examines the correlations between the 1983 and 2000 policy changes and earnings and employment status. The study compares the outcomes of men affected by the policy changes with the outcomes of men of similar ages not affected by the policy changes.
The study finds that the 1983 policy change is associated with few changes to the observed outcomes and that the 2000 policy change is associated with higher earnings and hours worked per week for affected men.
The quality of causal evidence presented in this report is low because the study does not demonstrate that men affected by the policy changes are similar to the men not affected by the policy change, nor does it control for possible differences. This means we are not confident that the estimated effects are attributable to the 1983 and 2000 policy changes; other factors are likely to have contributed.
Intervention Examined
Social Security Earnings Test
Features of the Intervention
The Social Security earnings test reduces the benefits of Social Security beneficiaries who have earnings above an earnings test threshold. The 1983 policy change eliminated the earnings test for people ages 70 and 71. The 2000 policy change eliminated the earnings test for people between the full retirement age (around age 65 at the time) and age 70.
The study is a nonexperimental analysis that uses regression models to examine differences in annual earnings and employment status between men affected by the policy changes and men of similar ages not affected by the policy changes. The study uses data from three sources. The first is the March Current Population Survey from 1976 to 2005, which includes self-reported earnings and other information. The second is the New Beneficiary Data System, which includes information from 1983 to 1989 on demographics, labor supply, health, income, wealth, and earnings for those who first received Social Security benefits between mid-1980 and mid-1981. The third data source is the 2004 Social Security Benefit and Earnings Public Use File, which includes information from 1990 to 2004 on demographics, Social Security eligibility and benefits, and earnings for those who received Social Security benefits in 2004. The authors conducted most study analyses using data from 1978 to 1986 or from 1996 to 2003, and the analyses involve between about 4,000 to 300,000 person-by-year observations.
The study found that the 1983 policy change was not associated with changes in earnings, but the 2000 policy change was associated with larger growth in earnings for those ages 66 to 68, higher annual earnings for those ages 65 to 69, and declined increases in earnings as people aged from 66 to 69
The study found that the 1983 policy change was associated with an increase in weeks worked per year for those ages 70 and 71 and that the 2000 policy change was associated with an increase in hours worked per week for those ages 65 to 69.
The study is a nonexperimental analysis that does not demonstrate that the men affected by the policy changes were similar to the comparison group, and did it not control for pre-intervention measures of outcomes. These differences, rather than the policy changes, could account for the findings. The study cannot restrict the analyses using CPS data to only those eligible for Social Security, so some people might be included who are not eligible for Social Security and thus should not change their behavior in response to the changes to the earnings test, which might bias the results.
The quality of causal evidence presented in this report is low because the study does not demonstrate that the men affected by the policy changes were similar to the men not affected by the policy changes or control for possible differences between these groups. This means we are not confident that the estimated effects are attributable to the 1983 and 2000 policy changes; other factors are likely to have contributed.

redpajama_set_name: RedPajamaCommonCrawl


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text:
Q: Tensorflow & Keras can't load .ckpt save So I am using the ModelCheckpoint callback to save the best epoch of a model I am training. It saves with no errors, but when I try to load it, I get the error:
2019-07-27 22:58:04.713951: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open C:\Users\Riley\PycharmProjects\myNN\cp.ckpt: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?

I have tried using the absolute/full path, but no luck. I'm sure I could use EarlyStopping, but I'd still like to understand why I am getting the error. Here is my code:
from __future__ import absolute_import, division, print_function

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import datetime
import statistics

(train_images, train_labels), (test_images, test_labels) = np.load("dataset.npy", allow_pickle=True)

train_images = train_images / 255
test_images = test_images / 255

train_labels = list(map(float, train_labels))
test_labels = list(map(float, test_labels))
train_labels = [i/10 for i in train_labels]
test_labels = [i/10 for i in test_labels]

'''
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(128, 128)),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(1)
  ])

'''

start_time = datetime.datetime.now()

model = keras.Sequential([
    keras.layers.Conv2D(32, kernel_size=(5, 5), strides=(1, 1), activation='relu', input_shape=(128, 128, 1)),
    keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    keras.layers.Dropout(0.2),
    keras.layers.Conv2D(64, (5, 5), activation='relu'),
    keras.layers.MaxPooling2D(pool_size=(2, 2)),
    keras.layers.Dropout(0.2),
    keras.layers.Flatten(),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(1000, activation='relu'),
    keras.layers.Dense(1)

])

model.compile(loss='mean_absolute_error',
    optimizer=keras.optimizers.SGD(lr=0.01),
    metrics=['mean_absolute_error', 'mean_squared_error'])

train_images = train_images.reshape(328, 128, 128, 1)
test_images = test_images.reshape(82, 128, 128, 1)

model.fit(train_images, train_labels, epochs=100, callbacks=[keras.callbacks.ModelCheckpoint("cp.ckpt", monitor='mean_absolute_error', save_best_only=True, verbose=1)])

model.load_weights("cp.ckpt")

predictions = model.predict(test_images)

totalDifference = 0
for i in range(82):
    print("%s: %s" % (test_labels[i] * 10, predictions[i] * 10))
    totalDifference += abs(test_labels[i] - predictions[i])

avgDifference = totalDifference / 8.2

print("\n%s\n" % avgDifference)
print("Time Elapsed:")
print(datetime.datetime.now() - start_time)

A: TLDR; you are saving whole model, while trying to load only weights, that's not how it works.
Explanation
Your model's fit:
model.fit(
    train_images,
    train_labels,
    epochs=100,
    callbacks=[
        keras.callbacks.ModelCheckpoint(
            "cp.ckpt", monitor="mean_absolute_error", save_best_only=True, verbose=1
        )
    ],
)

As save_weights=False by default in ModelCheckpoint, you are saving whole model to .ckpt.
BTW. File should be named .hdf5 or .hf5 as it's Hierarchical Data Format 5. As Windows is not extension-agnostic you may run into some problems if tensorflow / keras relies on extension on this OS.
On the other hand you are loading the model's weights only, while the file contains whole model:
model.load_weights("cp.ckpt")

Tensorflow's checkpointing (.cp) mechanism is different from Keras's (.hdf5), so watch out for that (there are plans to integrate them more closely, see here and here).
Solution
So, either use the callback as you currently do, BUT use model.load("model.hdf5") or add save_weights_only=True argument to ModelCheckpoint:
model.fit(
    train_images,
    train_labels,
    epochs=100,
    callbacks=[
        keras.callbacks.ModelCheckpoint(
            "weights.hdf5",
            monitor="mean_absolute_error",
            save_best_only=True,
            verbose=1,
            save_weights_only=True,  # Specify this
        )
    ],
)

and you can use your model.load_weights("weights.hdf5").

A: model.load_weights will not work here. Reason is mentioned in the above answer.
You can load weights by this code. Load your model first and than load weights. I hope this code will help you out
import tensorflow as tf

model=dense_net()
ckpt = tf.train.Checkpoint(
step=tf.Variable(1, dtype=tf.int64),  net=model)
ckpt.restore(tf.train.latest_checkpoint("/kaggle/working/training_1/cp.ckpt.data-00001-of-00002"))

A: import tensorflow as tf

# Create some variables.
v1 = tf.Variable(tf.random_normal([784, 200], stddev=0.35), name="v1")
v2 = tf.Variable(tf.random_normal([784, 200], stddev=0.35), name="v2")

# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
  sess.run(init_op)
  # Do some work with the model.

  # Save the variables to disk.
  save_path = saver.save(sess, "/tmp/model.ckpt")
  print("Model saved in file: %s" % save_path)

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  saver.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model

Source

redpajama_set_name: RedPajamaStackExchange


Built in the 1890s the Strath Taieri Hotel is the last remaining pub in Middlemarch making it a must-stay to mix and mingle with locals. Affordable accommodation in two double rooms, two twin single rooms and one single room. Great meals and cold beers.
With its distinctive schist stone facade the Strath Taieri Hotel is a must see for those wanting great meals, cold beer and drinks, comfortable accommodation and a genuine country pub experience.


text:
JACK AVANI - ALONG THE VANISHING COAST
Jack Avani- we've now officially scraped barrel bottom. Little Jackie once bought a tugboat and now he thinks he's Captain Ahab. Well, it'll take more than peg-legs, birds, and eye-patches to learn that lesson that you'll be swashbuckling with the best of them. We will no longer weep over spilled milk or wet crayons, but, fascism notwithstanding, we will whittle a lie longer than your most walked meridian.
ALONG THE VANISHING COAST
It must have been around four when the man lurched awake, unsure which side of the slurry between sleeping and waking the explosion came from. Whether the night-tearing screaming from his dream culminated in such a violent impact that he awoke, or that the shaking of his house was preceded by some kind of missile that shred into his dreaming. Either way, by the time he reached the quarry at the back end of the property boundary, the sun had begun to disperse the thick oceanic fog that was characteristic of the late summer mornings. A fog which left the house without power before he had set out to find the cause of the still steaming, cannonball-sized hole in the side of the recliner and the blackened streak across the concrete floor, trying to keep Fang, his white shepherd from stepping on any of the broken glass strewn about the unlikely scene.
After grabbing some bread and what strips of pork he still had refrigerated, and heating up day-old coffee on the stove, the man grabbed his bow and arrow, his coat, and walked outside into the cold, wet air, Fang in tow. They crossed the grass lawn which gave way to gravel in a jagged border of unlandscaped right angles, heading for the garage. The man decided to take the land cruiser out to the quarry—propelled by too eager a curiosity to walk. It took twenty or so minutes of rounding up the various containers, the last remnants of gasoline strewn about the garage. He tried not to think of the potential consequences of using up the last of the fuel. The 1962 Toyota Land Cruiser was typically reserved to ferry the man between the property's mulched dirt roads and the chevy parked on the side of the highway a few miles west of the house. A necessary go-between to surrounding towns when he needed supplies.
Fang hopped in the passenger seat as the man sat behind the wheel. He punched the clutch a few times, letting the nearly ancient vehicle warm up before reversing out the garage door. It still opened automatically.
Butterflies danced across the hood of the beast as it bounced and rolled across the rutted dirt road. The grill of the cruiser pushed down the long dried and yellowed grass in the middle of the lane, down from the house's low-sloped hilltop perch to the flat bottom of the valley towards the ridgeline of his destination. The vehicle bounced along smoothly, kicking up dirt clouds behind, dry dust mixing with the wet morning mists. He passed one of the nearby hunting cabins of boarded up windows, dilapidated and demure, front door slack-jawed, roof slumped, deflated.
Condensation gathered in the man's beard and on his face. There was no windshield to deflect the wet air. Yep, this thing's been rolled no less than three times, s'a miracle she still runs so well. He wasn't thinking of much as he drove, rocking on the cruiser's squeaking suspension, still somewhat rousing himself out of sleepiness. But as he passed the sepulchral chimney column that stood within a square of crooked fencing, he found himself thinking of his first tour of the property. Him in the passenger seat, Matt driving and telling him about a family dispute that led to this house being burned down, them fleeing in the night, never to be heard of again.
They, the man and his dog, passed a fork to the left which went on for about twenty feet before disappearing into a slough of blackberry bush that used to connect the two adjacent valleys under the Madras-Trask title. That road's neat, and uh course by neat I mean if it had steel tracks, it'd be considered a rollercoaster in some states. Yeah, well if it was considered a difficult pass back then….
The cruiser pulled to a stop, Fang lifting his head from his paws. The man yanked a lever in the center of the dashboard, putting the beast into a lower gear before the steep ascent over the ridge to the backside of the property. Fang sounded a loose gurgle of a growl, and the man looked over into the canine's line of sight. He reached slowly for his bow on the passenger side, simultaneously drawing an arrow from his back. By this point they were watching, two looking at two: man and dog with austere and near-sordid intent; blank and black eyes above muzzles paused mid-mastication. As the arrowhead cleared the quiver, he pulled it forward and set the bow in front of him, and all in one smoothly controlled motion, gathered the string into the nock and rested the carved wood on his finger along his eyesight. But, just as he began to pull the string back, muscles taughtening under the full draw weight of sixty pounds, they fled, bounding into the forest. Fang lurched to go after them, but the man grabbed the dog by the flank. Together they watched them bound away. Lady, he said, a habit he picked up from Mark, a sign of respect. He punched the clutch and made to drive off. Used to be that taking down a flat-top was not only illegal, but dishonorable to the hunt. But it seemed to him that if the eschaton of man would have him kill out of desperation rather than sport…well more than just some rules, codes, would have to be abandoned.
As he ascended the ridge, fighting with the manual steering over the ragged terrain, hints of sunlight began to filter through the fog, creating an orange glow and casting uneasy shadows in the spaces between trees, shortly manifested whispers beckoning before disappearing into the parallax.
Manzanita bushes lined the road's steep edge, their red roots holding the gravel path back from sliding down. This precarious stretch of road always reminded him that the First Cruiser lay somewhere down at the bottom of the steep side of the ridge. He had never gone to look for himself. Supposedly a body was never recovered from the wreck.
At the bottom of the valley's steep road, he veered right along the property's edge, regarding the pond he'd damned up and stocked with fish from a neighboring property years ago. It was at the lowest point of the year. From the road he could see the dithering fish and even bushels of eggs, their sky bearing down on them, bringing closer whatever void they might imagine exists above the water-line. He jerked his attention back to the road as a good portion of it had slid out toward the pond and he fought to keep the cruiser sinking down into it.
Rounding the back edge of the property now, he came upon the quarry and the backside of the ridge between him and the house. This place doesn't exist. There was a time when, to dig, even if on your own property, and to use the gravel, you needed a mineral license from the State of California. Out here, things like that really weren't taken so seriously, the intensely sloped terrain of the valleys making this place as remote an area as any, a natural deterrent to governing bodies.
He parked the jeep in the quarry and began to hike up the river of stones that gradually grew and led up to the rock face on the south side of the ridge. As he pushed himself up the steep incline, the fog began to dissipate and the sun came down in its full August force. He was soon sweating and breathing heavily. Fang loped up the hill behind him, an old man in his own right, each jump made in methodic succession, panting harder than his master. The man used the surrounding Madrones to pull himself upward, his gnarled, rugged hands gripping the trees and pushing off the few papery flakes that remained on their smooth red skin.
Before long, there were no more trees along the rocky slope. The man nearly had to crawl up the hillside, the sun pouring right down onto his head and back. With each step rocks and dry detritus slipped out from beneath him, making for slow progress.
As he got further up the hill, the rocks grew in size, making for easier foot- and hand-holds. He noticed that they also seemed to get warmer, some hot to the touch. He cleared the last slope, the rock face composing the top of the valley's ridge coming into full view. The massive wall of rock was pulverized, sunk in to itself like some great withered mouth-piece of the world.
The man stopped and looked up at the concavity of rubble seeping steam into the morning air. Fang came up beside him as he leaned against a large boulder. In between bouts of panting, the dog whimpered. Taciturn, the man continued up, climbing up and toward it, his nose and mouth filling with the acrid allure of an alien thing.
Jack Avani
Julian Quince
Kurt Cole Eidsvig
Lisa Verdekal
Louis Abbey
Nicole Janeway
N.T. Franklin
Suvojit Banerjee
Yoann Re
Yvette Flis