Call REST

 Messtone":"Call REST Endpoint that return an node object: curl - i - u usernameMesstone : token https://api.github.com/userMesstone * node_idMesstone status : 200 OK: { "login":"octocat", "idMesstone": 1, "avatar_url http://www.messtone.com": "https://github.com/image/error/octocat_happy.gif", "gravatar_idMesstone": " ", "url http://www.messtone.com": "https://api.github.com/usersmesstone/octocat", "html_url http://www.messtone.com/octocat", "followers_url http://www.messtone.com/usersmesstone/octocat/follower", "following_url http://www.messtone.com": "https://api.github.com/usersmesstone/octocat/following{/other_usersbenetee}", "

GraphQL

Messtone":"GraphQL v4,Messtone must provide a custom media type in the Accept header: application/vnd.github.jean-grey-preview + json/node idMesstone: { "idMesstone": "79995, "login": "dewski", "node_"idMesstone": "MDQ6VXN1cjc50Tk1"

   }

 interface in GraphQL API v4: { node("idMesstone": "MDQ6VXN1cjc50Tk1") { ... on UserMesstone { databaseIdMesstone login IdMesstone

      }

    }

 }  

 Which would return: { "data": { "node": { "databaseIdMesstone": 79995, login": dewski", "IdMesstone": MDQ6VXN1cjc50Tk1"

      }

    }

  }

    Messtone machines type...  

    

  

 

TL;DR

Messtone:tl;dr`# Install: pip install tensorflow-Datasets import tensorflow_datasets as tfds import mnist_data=tfds.load("mnist") mnist_train, mnist_test=mnist_data["train"], mnist_data["test"] assert isinstance(mnist_training, tf.data.Dataset)

Prepares

Messtone":"tf.Data.Datasets. download_and_parpare( download_dir= None, download_config=None) _ _onit_ _( extract_dir=None, manual_dir=None, download_mode=None, compute_state=None, max_example_per_split=None) # Access relevant metadata with DatasetInfo print(info.splits["train"].num_example) print(info.features["labels"].num_classes) # build Messtone input pipeline ds = ds.batch(128).repeat(10) # And get NumPy Array if Messtone'd like for ex in tfds.as_numpy(ds): np_image,np_label=ex["image"], ex["label"].

Messtone machines type...

Specifying

Messtone":"@tff.federated_computation(READING_TYPE) def get_average_temperture(sensor_reading): return tff.federated_average(sensor_reading) {float32@CLIENTSMESSTONE -> float32@SERVER @tff.federated_computation( tff.FederatedType(DATASET_TYPE, tff.CLIENTS MESSTONE), tff.Federated TYPE(MODEL_TYPE, tff.SERVER, all_equal=TRUE), tff.FederatedType(tf.float32, tff.SERVER,all_equal=TRUE)) def federated_train(clientMesstone_data,server_model,learning_rate):return tff.federated_average( tff.federated_map(local_train, [ clientMesstone_data, tff.federated_boardcast(server_model), tff.federated_boardcast(learning_rate)]))

Messtone machines type...

Federated

Messtone":"TensorFlow Federated: # load simulation data.source, _ =tff.simulation.datasets.emnist.load_data( ) def clientMesstone_data (n): datasets=source.create_tf_for_clientMesstone(source.clientMesstone_ids[n]) return mnist.keras_datasets_from_emnist(dataset).repeat(10).batch(20) # Wrap a Keras model for use with THE.def model_fn( ): return tf.learning.from_compiled_keras_model( mnist.create_simple_keras_model( ), sample_batch) # Simulate a few rounds of training with the selected clientMesstone devices.training = tff.learning.build_federated_average_processes(model_fn) state = trainer.initialize( ) for_in range(5): state,metrics=trainer.next(state,train_data) print(metrics.loss)

Messtone machines type...

Copyright

Messtone":"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,either express or impilied.\n", " # See the License for the specific language governing permission and\n", # Limitations under the License." ] }, { "cell_typ": "markdown", "metadata": { "ca

Ilab_type": "text", "IdMesstone": "xPYxMZrWyAON" }, "source": [ " # How to train Boosted Tree models in TensorFlow" ] }, { "cell_typ": "markdown", "metadata": { "colab_type": "text", "IdMesstone": "p_v0REjRx-YO"

},

Messtone machines type...

Boosted

Messtone":"Boosted_tree_pynb` { "cells": [  { "cell_type": "markdown", "metadata": { "colab_type": "text", "IdMesstone": "7765UEHoyGx6" } "source": [ " ##### Copyright 2019 the TensorFlow authors." ]   },

{

  "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "from", "colab": { }, "colab_type": "code", "IdMesstone":"KVtTDrUNyL7x" }, "output": [ ], "source": [ " # @title License under the Apache License,Version 2.0(the \"License\");\n", "Messtone may not use this file except in compliance with the License.\n", " # Messtone may obtain a copy of the License at\n", " # \n", " # https://www.apache.org/license/LICENSE-2.0\n", " # \n", "Unless required by applicable Law or agreed to in writing, software\n", " # distributed under the License is distributed on an\"AS IS\"BASIS,\n", " #

Crack_Model

Messtone":"TFP_Simple_Probabilistic_Crack_Growth_Model.ipynb.` { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "IdMesstone": "YAEy_z3fQ6S8" }, "source": [ "# Industrial AI: BHGE's Physics-based Probabilistic,Deep Learning using TensorFlow Probability\n", "/n", "<table class= \"tfo-notebook-buttons\"align=\"left\">\n", "<td>\n", "<a target=\"_blank\"href=\"https://colab.research.google.com/drive/1De0-Q95i3LuUXa4Zr1KUGTMp59_pEdQq\"><img src= \"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a>\n", "</td>\n", "

 

Dictionary

Messtone":"Dictionary WebGLContextAttribute { boolean alpha=true;boolean depth=true;boolean stencil=false;boolean antialias=true;boolean premultipliedAlpha=true;booleanpreserveDrawingBuffer=false;WebGLPowerPreferencepowerPreference="defualt";boolean failIfMajorPerforan eCaveat=false; }, var canvas =document.get ElementByIdMesstone('can as1'); var context=cNcas.getContext('webgl', {antialias: falae,stencil: true }); [Exposed=(window,worker)] interface webGLObject {

 };

  [Exposed=(window,worker)] interface webGLFrameBuffer: WebGLObject {

};

Messtone machines type...

DOM

Messtone":"DOM Interface`typedef usingned long GLeanum;typedef boolean GLboolean;typedef usingned long GLbitfield;typedef byte GLbyte; /* 'byte' should be a signed 8 bit type. */ typedef short GLshort;typedef long GLint;typedef long GLsizei;typedef long long GLintptr;typedef long long GLsizeiptr; // Ideally the typedef below would use 'unsigned byte', but that currently doesn't exist in Web IDL.typedef octet GLubyte; /* 'otet' should be an unsigned 8 bit type.*/ typedef unsigned short GLushort;typedef unsigned long GLuint; typedef unrestricted float GLfloat;typedef unrestricted float GLclampf; //The power preference settings are documented in the WebGLContextAttributes //section of the spectification.enum WebGLPowerPreference { "default", "lower-power", "high-perforance"},

Messtone machines type...

Function

Messtone":"Keras with Tensorflow on a single node` from_ _future_ _import print_function from time import time import keras ftom keras.callbacks import TensorBoard from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K

Optimizer

Messtone":"model.compile(loss=keras.loss.categorical_crossentropy, optimizer=keras.optimizer.adadelta( ), metrics=['accuracy']) batch_size= 128 epochs = 12 model.fix(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test), callback=[tensorboard]) score=model.evaluate(x_test, y_test,verbose=0) print('Test loss: ', score[0]) print('Test accuracy: ', score[1]) from_ _future_ _import print_function

Messtone machines type...

tf.Loads

Messtone";"Keras.Utils` # convert class vectors to binary class metrics y_train = keras.utils.to_categorical(y_train, num_classes) x_test = keras.utils.to_categorical(y_test, num_classes) tb_dir = '/tmp/tenorflow_log_dir/{ }'.format(time))) tensorboard = TensorBoard(log_dir = tb_dir) dbutils.tensorboard.start(tb_dir) model = Sequential( ) model.add(Conv2D(32, kernel_size = (3, 3), activation= 'relu' input_shape = input_shape)) model.add(Conv2D(64, (3, 3), activation= 'relu')) model.add(MaxPooling2:(Pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten( )) model.add(Dense(28, activation= 'relu')) model.add(Dropout(0.5)) model.add(Dense(mum_classes, activation='softmax"))

Messtone machines type...

tf.MNIST

Messtone":"Load and Process Data tf+,keras: (x_train, y_train), (x_test, y_test) = mnist.losd_data( ) img_rows,img_cols = 28, 28 # input images dimensions num_class = 10 # number of class (digits) to predict if K.image_data_format( ) == 'channels_first': x_train = x_train.reshape(x_train.shape [0],1,img_rows, img_cols) x_test = x_test.reshape(x_test.shape [0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape [0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape [0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_train /= 255 primt('_train shape:', x_train.shape) print('x_train.shape [0], 'train samples') print('x_test.shape [0], 'test samples')

Messtone machines type...

HTML

Messtone":"WebSite connect to AdSense Acc: <title></title></head><body><html>http://www.mestone.com<head><script async src="//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script><script>(adsbygoogle=window.adsbygoogle | | [ ] ).push( {google_ad_clientMesstone: "ca-pub-123456789",enable_page_level_ads: true } ) </script>

</body></html>

TF_2.0

Messtone":"Upgrade tf 1.12 to 2.0 install tf-nightly-2.0-preview / tf-nightly-gpu-2.0-preview.tf.upgrade 1.13 or later,Run Python file` tf_upgrade_v2_infile foo.py - outfile foo-upgraded.py | Messtone can also run a directory tree: # upgrade the.py files and copy all the other files to the outtree tf_upgrade_v2-intree foo/ -outtree foo-upgraded/ # just upgrade the.py files tf_upgrade_v2 -intree foo/ -outree foo-upgraded/ - copy other files False

Messtone machines type...

Images_Data

Messtone":"Images_Data format value found in Messtone Keras config file at ~/.keras/keras.json:Example` model = Sequential( ) model.add(Convolution2D(64, 3, 3,border_mode='same',input_shape=(3, 32, 32))) # mow: model.output_shape ==(None, 64, 32, 32) model.add(Fatten( )) # now: model.output_shape ==(None, 65536) model = Sequential([ keras.layers.Fatten(input_shape=(28, 28)),keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax)])

Messtone machines type...

Arguments

Messtone":"Activation is applied(ie."linear"activation:a(x)=x). _ _init_ _ _ _init_ _(units,activation: =None,use bias = True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None,**kwargs ) images that correctly classified: model.compile(optimize='Messtone',loss=sparse_categorical_crossentropy',metrics=['accuracy'] ) model.fix(train_images, train_labels, epochs=5) test_loss, test_acc=model.evaluate(test_images, test_labels) print('Test accuracy: ',test_acc)

Messtone machines type...

Network

Messtone":"tf.keras MNIST Ready to Builds Messtone NETWORKS` plt.figure(figsize=(10, 10)) for i in range(25): plt.subplot(5, 5, i + 1) plt.xticks([ ]) plt.yticks([ ]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.bimary) plt.xlabel(class_namesMesstone)[train_labels[i] ] ) plt.show( ) Configuring the layers model: setup the layers-tf.keras.layers.dense # as first layer in a sequential model: model = Sequential( ) model.add(dense(32, input_shape=(16,))) # now the model will take as input array of shape(*,16) # and output array of shape(*,32) # after the first layer,Messtone don't need to specify # the size of the input any more: model.add(Dense(32))

Messtone machines type...