Copy of Assignment1-TF_basics_handout.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copy of Assignment1-TF_basics_handout.ipynb","version":"0.3.2","provenance":[{"file_id":"1h6hZ7GcSfoYGyR5Kf_r74z-3WFfBP6xv","timestamp":1555178086756}],"collapsed_sections":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"BVsWqcvC1iDa","colab_type":"text"},"cell_type":"markdown","source":["# \"Hello TensorFlow!\" assignment\n","\n","To get to know the wolrd of TensorFlow, let's play through a \"hello world\" level task!"]},{"metadata":{"id":"Lua2MFZc1iDb","colab_type":"text"},"cell_type":"markdown","source":["## Task 1. define and add together two constant values!\n","\n","- Import TensorFlow under the customary name tf\n","- Define two constants of arbitrary name\n","- Give them a value, one of them should be 31 as integer, the other should be 11\n","- Add them together and store the result of them into the variable `meaning_of_life` \n","- (observe, that there are two ways to add things - think \"operator overload\")\n","- Let's not leave resources open! (context...)\n","- Print the `meaning_of_life`! :-P"]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:02.453187Z","start_time":"2019-03-21T06:00:01.573191Z"},"id":"H8cpycWv1iDc","colab_type":"code","outputId":"3126e182-720a-4df3-95f5-82ae7cf07888","executionInfo":{"status":"ok","timestamp":1555322007837,"user_tz":-120,"elapsed":2057,"user":{"displayName":"ananya neogi","photoUrl":"https://lh5.googleusercontent.com/-oYos_5uEiio/AAAAAAAAAAI/AAAAAAAAM8U/bI2Bm0P0epw/s64/photo.jpg","userId":"00462567981712017677"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["import tensorflow as tf\n","\n","tensor1 = tf.constant(31, dtype=tf.int32)\n","tensor2 = tf.constant(11, dtype=tf.int32)\n","\n","addition = tensor1 + tensor2\n","\n","with tf.Session() as sess:\n","  meaning_of_life = sess.run(addition)\n","\n","print(\"The meaning of life is:\",meaning_of_life)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["The meaning of life is: 42\n"],"name":"stdout"}]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:02.457031Z","start_time":"2019-03-21T06:00:02.455039Z"},"id":"Jo_sWCkK1iDe","colab_type":"code","colab":{}},"cell_type":"code","source":["assert meaning_of_life==42"],"execution_count":0,"outputs":[]},{"metadata":{"id":"tsfsEdR81iDi","colab_type":"text"},"cell_type":"markdown","source":["## Task 2.: Use input values!\n","\n","- Ask for a positive integer from the console (use Python3's default input procedure) and store the value to the `noise_input` variable\n","- Again define an addition, that adds together the previously added two constants and the recently inputted value\n","- _Please don't define constants again, reuse them_\n","- Please observe, that for the \"inputs\" we have to also define a type\n","- Execute the operation with the input value and store the result in the variable `more_noise_to_meaning`\n","- Print the result!"]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:04.246810Z","start_time":"2019-03-21T06:00:02.458211Z"},"id":"oc3Hk2Ga1iDi","colab_type":"code","outputId":"b080afd0-a5c3-48d8-a281-42011a5f676c","executionInfo":{"status":"ok","timestamp":1555322717905,"user_tz":-120,"elapsed":5128,"user":{"displayName":"ananya neogi","photoUrl":"https://lh5.googleusercontent.com/-oYos_5uEiio/AAAAAAAAAAI/AAAAAAAAM8U/bI2Bm0P0epw/s64/photo.jpg","userId":"00462567981712017677"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["noise_input_variable = int(input(\"enter a positive integer:\"),32)\n","\n","add1 = tensor1 + tensor2 + noise_input_variable\n","\n","with tf.Session() as sess:\n","  more_noise_to_meaning = sess.run(add1)\n","\n","print(more_noise_to_meaning)"],"execution_count":2,"outputs":[{"output_type":"stream","text":["enter a positive integer:4\n","46\n"],"name":"stdout"}]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:04.252994Z","start_time":"2019-03-21T06:00:04.249891Z"},"id":"KC9Hz0l11iDk","colab_type":"code","colab":{}},"cell_type":"code","source":["assert more_noise_to_meaning>42"],"execution_count":0,"outputs":[]},{"metadata":{"id":"gEZY6hLQ1iDn","colab_type":"text"},"cell_type":"markdown","source":["## Task 3.:  Looking for the variable's value\n","\n","- Define a _tf_ `Variable` named `noise`\n","- Don't forget to initialize it's value, to 1\n","- Define an operation `remove_noise_transform` which from the _previous_ addition operation (that we defined in the prior cell) substracts the value of the `noise` (the tf.Variable)\n","- With the help of the `tf.assign` function crate an `increase_op`, which increases the value of `noise` with 1\n","- Use a context and initialize the variables\n","- In the while cycle assign value to the `result` by running a `tf.session`\n","    - Retrieve the value of the `remove_noise_transform` and the `increase_op` into the `result` list (in this order!) \n","    - Since in our graph the `a_placeholder` is a node, we have to again give `noise_input` to it so that it can operate!\n"]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:04.289214Z","start_time":"2019-03-21T06:00:04.257390Z"},"id":"fjzVMjw11iDo","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":119},"outputId":"8b5e414f-1d0f-413e-93f2-569354107092","executionInfo":{"status":"ok","timestamp":1555328334761,"user_tz":-120,"elapsed":8762,"user":{"displayName":"ananya neogi","photoUrl":"https://lh5.googleusercontent.com/-oYos_5uEiio/AAAAAAAAAAI/AAAAAAAAM8U/bI2Bm0P0epw/s64/photo.jpg","userId":"00462567981712017677"}}},"cell_type":"code","source":["noise = tf.Variable(1, dtype=tf.int32)\n","\n","remove_noise_transform = add1 - noise\n","\n","increase_op = tf.assign(noise , noise+1)\n","\n","init = tf.global_variables_initializer()\n","result =[1000]\n","\n","with tf.Session() as sess:\n","    sess.run(init)\n","    \n","    while result[0]>42:\n","        noise_input = int(input(\"enter a positive integer:\"),32)\n","        result = sess.run([remove_noise_transform, increase_op])\n","        print(result)\n"],"execution_count":16,"outputs":[{"output_type":"stream","text":["enter a positive integer:3\n","[45, 2]\n","enter a positive integer:0\n","[44, 3]\n","enter a positive integer:0\n","[42, 4]\n"],"name":"stdout"}]},{"metadata":{"ExecuteTime":{"end_time":"2019-03-21T06:00:04.294125Z","start_time":"2019-03-21T06:00:04.290790Z"},"id":"RTdIX0a61iDq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"6b989315-ce94-4edf-c77b-91e947683656","executionInfo":{"status":"ok","timestamp":1555328337602,"user_tz":-120,"elapsed":610,"user":{"displayName":"ananya neogi","photoUrl":"https://lh5.googleusercontent.com/-oYos_5uEiio/AAAAAAAAAAI/AAAAAAAAM8U/bI2Bm0P0epw/s64/photo.jpg","userId":"00462567981712017677"}}},"cell_type":"code","source":["assert result[0]==42\n","assert result[1]>int(noise_input)\n","\n","print(\"Congrats, success!!!!\")"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Congrats, success!!!!\n"],"name":"stdout"}]}]}