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+ {
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+ "nbformat" : 4 ,
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+ "nbformat_minor" : 0 ,
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+ "metadata" : {
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+ "colab" : {
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+ "provenance" : [],
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+ "authorship_tag" : " ABX9TyO0fxb6omsPqcaelthdhOlo" ,
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+ "include_colab_link" : true
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+ },
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+ "kernelspec" : {
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+ "name" : " python3" ,
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+ "display_name" : " Python 3"
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+ },
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+ "language_info" : {
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+ "name" : " python"
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+ }
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+ },
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+ "cells" : [
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+ {
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+ "cell_type" : " markdown" ,
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+ "metadata" : {
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+ "id" : " view-in-github" ,
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+ "colab_type" : " text"
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+ },
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+ "source" : [
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+ " <a href=\" https://colab.research.google.com/github/iamanolive/code-harbor-hub/blob/main/tensorflow-tutorials/introduction.ipynb\" target=\" _parent\" ><img src=\" https://colab.research.google.com/assets/colab-badge.svg\" alt=\" Open In Colab\" /></a>"
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " import tensorflow as tf"
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+ ],
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+ "metadata" : {
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+ "id" : " A2iC7nlsQ_tU"
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+ },
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+ "execution_count" : 8 ,
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+ "outputs" : []
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "source" : [
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+ " # creating tensors"
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+ ],
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+ "metadata" : {
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+ "id" : " d3SLZ6TiR4Zw"
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+ }
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " string = tf.Variable(\" this is a string\" , tf.string)\n " ,
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+ " number = tf.Variable(324, tf.int16)\n " ,
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+ " floating = tf.Variable(3.567, tf.float64)"
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+ ],
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+ "metadata" : {
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+ "id" : " nW0NfMdPVmDo"
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+ },
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+ "execution_count" : 9 ,
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+ "outputs" : []
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "source" : [
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+ " # ranks or degrees of tensors"
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+ ],
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+ "metadata" : {
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+ "id" : " njzcqxqsWDpc"
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+ }
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " rank1_tensor = tf.Variable([\" Test\" , \" Ok\" , \" Somebody\" , \" Somebody\" ], tf.string)\n " ,
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+ " rank2_tensor = tf.Variable([[\" test\" , \" ok\" ], [\" test\" , \" yes\" ], [\" ok\" , \" ok\" ]], tf.string)"
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+ ],
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+ "metadata" : {
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+ "id" : " jY_yrAmUVxmp"
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+ },
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+ "execution_count" : 27 ,
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+ "outputs" : []
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " tf.rank(rank1_tensor)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " JKhqMx7iWe7x" ,
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+ "outputId" : " 7f11b444-016e-4654-8436-6340e612def7"
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+ },
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+ "execution_count" : 28 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " execute_result" ,
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+ "data" : {
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+ "text/plain" : [
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+ " <tf.Tensor: shape=(), dtype=int32, numpy=1>"
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+ ]
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+ },
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+ "metadata" : {},
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+ "execution_count" : 28
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " tf.rank(rank2_tensor)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " sw5Ns_2YWsEo" ,
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+ "outputId" : " e96a3e35-d3b6-4056-e327-ca08c35c2795"
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+ },
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+ "execution_count" : 29 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " execute_result" ,
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+ "data" : {
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+ "text/plain" : [
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+ " <tf.Tensor: shape=(), dtype=int32, numpy=2>"
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+ ]
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+ },
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+ "metadata" : {},
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+ "execution_count" : 29
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " tf.rank(number)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " 4qSdqk-aXRO1" ,
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+ "outputId" : " 41573023-fe3c-44a9-8f15-4e119d58fec3"
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+ },
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+ "execution_count" : 30 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " execute_result" ,
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+ "data" : {
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+ "text/plain" : [
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+ " <tf.Tensor: shape=(), dtype=int32, numpy=0>"
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+ ]
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+ },
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+ "metadata" : {},
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+ "execution_count" : 30
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "source" : [
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+ " # shapes of tensors"
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+ ],
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+ "metadata" : {
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+ "id" : " JSh-sGGtXFbd"
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+ }
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " rank1_tensor.shape"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " lKeKVZ0DXXXo" ,
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+ "outputId" : " 9bdef519-f5b6-42fd-af37-93f0d86f51f8"
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+ },
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+ "execution_count" : 31 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " execute_result" ,
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+ "data" : {
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+ "text/plain" : [
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+ " TensorShape([4])"
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+ ]
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+ },
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+ "metadata" : {},
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+ "execution_count" : 31
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " rank2_tensor.shape"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " Nq55Y62_XH2C" ,
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+ "outputId" : " d662fd43-8d3a-4ad5-e894-67e3e5d34557"
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+ },
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+ "execution_count" : 32 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " execute_result" ,
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+ "data" : {
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+ "text/plain" : [
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+ " TensorShape([3, 2])"
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+ ]
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+ },
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+ "metadata" : {},
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+ "execution_count" : 32
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "source" : [
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+ " # changing shapes"
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+ ],
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+ "metadata" : {
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+ "id" : " fZq-ciAuX9nd"
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+ }
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " tensor1 = tf.ones([1, 2, 3])\n " ,
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+ " tensor2 = tf.reshape(tensor1, [2, 3, 1])\n " ,
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+ " tensor3 = tf.reshape(tensor2, [3, -1])"
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+ ],
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+ "metadata" : {
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+ "id" : " 6B220CYWYA-r"
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+ },
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+ "execution_count" : 37 ,
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+ "outputs" : []
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " print(tensor1)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " yk7p22RyYS2I" ,
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+ "outputId" : " c5d3f951-7ba4-4f10-f76f-508d7efadbff"
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+ },
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+ "execution_count" : 38 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " stream" ,
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+ "name" : " stdout" ,
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+ "text" : [
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+ " tf.Tensor(\n " ,
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+ " [[[1. 1. 1.]\n " ,
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+ " [1. 1. 1.]]], shape=(1, 2, 3), dtype=float32)\n "
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+ ]
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " print(tensor2)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " 121lHMJYYVG3" ,
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+ "outputId" : " 0b1d5684-ce45-4f6d-a108-1a54ba008f6d"
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+ },
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+ "execution_count" : 39 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " stream" ,
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+ "name" : " stdout" ,
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+ "text" : [
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+ " tf.Tensor(\n " ,
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+ " [[[1.]\n " ,
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+ " [1.]\n " ,
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+ " [1.]]\n " ,
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+ " \n " ,
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+ " [[1.]\n " ,
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+ " [1.]\n " ,
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+ " [1.]]], shape=(2, 3, 1), dtype=float32)\n "
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+ ]
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "source" : [
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+ " print(tensor3)"
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+ ],
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+ "metadata" : {
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+ "colab" : {
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+ "base_uri" : " https://localhost:8080/"
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+ },
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+ "id" : " UnbSuXgxYWiw" ,
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+ "outputId" : " 9c1f3139-6863-47a9-f95b-067fc158a914"
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+ },
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+ "execution_count" : 44 ,
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+ "outputs" : [
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+ {
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+ "output_type" : " stream" ,
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+ "name" : " stdout" ,
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+ "text" : [
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+ " tf.Tensor(\n " ,
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+ " [[1. 1.]\n " ,
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+ " [1. 1.]\n " ,
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+ " [1. 1.]], shape=(3, 2), dtype=float32)\n "
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+ ]
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+ }
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+ ]
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+ }
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+ ]
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+ }
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