{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Neural_network_Mnist_Dataset.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"77SSSwx1yiCQ","colab_type":"code","outputId":"8d3c5c95-0a98-4a77-d7fe-abd253f193a2","executionInfo":{"status":"ok","timestamp":1572961471098,"user_tz":-330,"elapsed":2481,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":80}},"source":["import tensorflow as tf"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"BZFEQNv2zvTc","colab_type":"code","colab":{}},"source":["from tensorflow.examples.tutorials.mnist import input_data"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SeQBBhaNz-eL","colab_type":"code","outputId":"b07f8611-c5ca-4734-ecff-257c1bfb8c3d","executionInfo":{"status":"ok","timestamp":1572961576091,"user_tz":-330,"elapsed":1501,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":534}},"source":["mnist = input_data.read_data_sets(\"MNIST_data/\" , one_hot=True)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From <ipython-input-4-c6bc8264dab0>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please write your own downloading logic.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use urllib or similar directly.\n","Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.data to implement this functionality.\n","Extracting MNIST_data/train-images-idx3-ubyte.gz\n","Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.data to implement this functionality.\n","Extracting MNIST_data/train-labels-idx1-ubyte.gz\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.one_hot on tensors.\n","Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n","Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n","Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n","Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"RVi_Aumn0N5z","colab_type":"code","outputId":"77dd0e76-ad94-4b97-cc94-b9365143ce95","executionInfo":{"status":"ok","timestamp":1572961688834,"user_tz":-330,"elapsed":827,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["type(mnist)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensorflow.contrib.learn.python.learn.datasets.base.Datasets"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"bvRf0B-I01DL","colab_type":"code","outputId":"5cc3f2ba-53f1-45d4-f268-619d80e3cec8","executionInfo":{"status":"ok","timestamp":1572961746349,"user_tz":-330,"elapsed":848,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":137}},"source":["mnist.train.images"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       ...,\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"hWEh9KXe0pmK","colab_type":"code","outputId":"bc11c7e5-cddd-439a-ea1a-6d5bdf7a06b1","executionInfo":{"status":"ok","timestamp":1572961729558,"user_tz":-330,"elapsed":872,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mnist.train.num_examples"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["55000"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"nMDBHJlf0ur6","colab_type":"code","outputId":"ad04c45a-b868-43f6-e51c-fd95863a3a26","executionInfo":{"status":"ok","timestamp":1572961820396,"user_tz":-330,"elapsed":1111,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mnist.test.num_examples"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"idFLPhYd1Jg6","colab_type":"code","colab":{}},"source":["#visualize the data\n","\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ftbQKAig1SjE","colab_type":"code","outputId":"32782700-cb36-4ac4-9f10-3f285085fb0d","executionInfo":{"status":"ok","timestamp":1572961939986,"user_tz":-330,"elapsed":654,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mnist.train.images[1].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(784,)"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"85iLnyb51Sgi","colab_type":"code","outputId":"c4f3fcce-c8c1-4ccd-d8f1-a463be74a93c","executionInfo":{"status":"ok","timestamp":1572961951358,"user_tz":-330,"elapsed":922,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["plt.imshow(mnist.train.images[1].reshape(28,28))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f69878d6e80>"]},"metadata":{"tags":[]},"execution_count":15},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"qffe3-pL1Sda","colab_type":"code","outputId":"5c949a68-ebad-42e5-8d4e-5ee7b8be6c3f","executionInfo":{"status":"ok","timestamp":1572962013666,"user_tz":-330,"elapsed":879,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["plt.imshow(mnist.train.images[34543].reshape(28,28))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f69874023c8>"]},"metadata":{"tags":[]},"execution_count":17},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQdiBJAg7kARhB5Ig7EAShB1IgrADSfwficEmDCNfB4kAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Kt-Bg2WH1SY6","colab_type":"code","outputId":"e8ffab7b-b93d-4a6c-d639-f412b91f6b70","executionInfo":{"status":"ok","timestamp":1572962094155,"user_tz":-330,"elapsed":976,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mARQycAY7YzxPBbOEwWXoHNL-9_IUZnVdV5lsgliBk=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["plt.imshow(mnist.train.images[1].reshape(28,28), cmap='gist_gray')              "],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f698735d588>"]},"metadata":{"tags":[]},"execution_count":18},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6J-EdVg71SKC","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}