Pythonista7

package main

import (
	"fmt"
	"math"
	"math/rand"
	"sync"
	"sync/atomic"
	"time"

Pythonista7

package main

import (
	"encoding/json"
	"fmt"
	"net/http"
	"os"
	"sync"
	"time"
)

Pythonista7

for i in $(find ./ -name '*.tsx')
do
  if ! grep -q Copyright $i
  then
    cat copyright.txt $i >$i.new && mv $i.new $i
  fi
done

Pythonista7

# Common build stage
FROM node:14.14.0-alpine3.12 as common-build-stage

COPY . ./app

WORKDIR /app

RUN npm install

EXPOSE 3000

Pythonista7

W = tf.Variable(tf.ones([num_features, num_classes]), name="weight")
b = tf.Variable(tf.zeros([num_classes]), name="bias")

# Comment 1
def logistic_regression(x):
    return tf.nn.softmax(tf.matmul(x, W) + b)
# Comment 2
def cross_entropy(y_pred, y_true):
    y_true = tf.one_hot(y_true, depth=num_classes)
    y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)

Pythonista7

# Weight of shape [784, 10], the 28*28 image features, and total number of classes.
W = tf.Variable(tf.ones([num_features, num_classes]), name="weight")
# Bias of shape [10], the total number of classes.
b = tf.Variable(tf.zeros([num_classes]), name="bias")

# Logistic regression (Wx + b).
def logistic_regression(x):
    # Apply softmax to normalize the logits to a probability distribution.
    return tf.nn.softmax(tf.matmul(x, W) + b)

Pythonista7

x = tf.constant(3.0)
with tf.GradientTape(persistent=True) as g:
  g.watch(x)
  y = x * x
  z = y * y
dz_dx = g.gradient(z, x)  # (4*x^3 at x = 3)
print(dz_dx)
# tf.Tensor(108.0, shape=(), dtype=float32)
dy_dx = g.gradient(y, x)
print(dy_dx)

Pythonista7

with tf.GradientTape() as g:
  g.watch(x)
  with tf.GradientTape() as gg:
    gg.watch(x)
    y = x * x
  dy_dx = gg.gradient(y, x)  # dy_dx = 2 * x
d2y_dx2 = g.gradient(dy_dx, x)  # d2y_dx2 = 2

Pythonista7

/* eslint-disable no-use-before-define */
interface SlidevConfig {
  title: string
  /**
   * String template to compose title
   *
   * @example "%s - Slidev" - to suffix " - Slidev" to all pages
   * @defult '%s'
   */
  titleTemplate: string

Pythonista7

def linear_regression(x):
    return W * x + b
  
def mean_square(y_pred,y_true):
  return tf.reduce_mean(tf.square(y_pred-y_true))

optimizers = tf.optimizers.SGD(learning_rate=0.01)

def run_optimization():
  with tf.GradientTape() as g: