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SupreethRao99/colab-issue.ipynb

Created July 28, 2021 13:22
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colab-issue.ipynb
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colab-issue.ipynb
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"metadata": {
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"source": [
"<a href=\"https://colab.research.google.com/gist/SupreethRao99/e53e122883149326e82553a0cf7e6811/colab-issue.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HfQEgmDcPqEI"
},
"source": [
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 222,
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"outputId": "2037d156-66aa-429b-da91-745cb4814f6e"
},
"source": [
"(ds_train, ds_test), ds_info = tfds.load(\n",
" 'mnist',\n",
" split=['train', 'test'],\n",
" shuffle_files=True,\n",
" as_supervised=True,\n",
" with_info=True,\n",
")"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
"local data directory. If you'd instead prefer to read directly from our public\n",
"GCS bucket (recommended if you're running on GCP), you can instead pass\n",
"`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
"\n"
],
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},
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"text": [
"\n",
"\n",
"\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cNW4irM-QS2-"
},
"source": [
"def normalize_img(image, label):\n",
" \"\"\"Normalizes images: `uint8` -> `float32`.\"\"\"\n",
" return tf.cast(image, tf.float32) / 255., label\n",
"\n",
"ds_train = ds_train.map(normalize_img)\n",
"ds_train = ds_train.batch(128)"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "REFE87jhQTZ5"
},
"source": [
"ds_test = ds_test.map(normalize_img)\n",
"ds_test = ds_test.batch(128)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZfQu-SABQW5W"
},
"source": [
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
" tf.keras.layers.Dense(128,activation='relu'),\n",
" tf.keras.layers.Dense(10, activation='softmax')\n",
"])\n",
"model.compile(\n",
" loss='sparse_categorical_crossentropy',\n",
" optimizer=tf.keras.optimizers.Adam(0.001),\n",
" metrics=['accuracy']\n",
")"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZRhT_AU2QbWN",
"outputId": "0aa49946-0316-4a32-dc74-3cb55d1b8e50"
},
"source": [
"model.fit(ds_train,epochs=10,validation_data=ds_test)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"469/469 [==============================] - 10s 21ms/step - loss: 0.3597 - accuracy: 0.8998 - val_loss: 0.1909 - val_accuracy: 0.9456\n",
"Epoch 2/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.1650 - accuracy: 0.9535 - val_loss: 0.1381 - val_accuracy: 0.9596\n",
"Epoch 3/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.1184 - accuracy: 0.9664 - val_loss: 0.1133 - val_accuracy: 0.9667\n",
"Epoch 4/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0912 - accuracy: 0.9742 - val_loss: 0.1003 - val_accuracy: 0.9714\n",
"Epoch 5/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.0734 - accuracy: 0.9792 - val_loss: 0.0923 - val_accuracy: 0.9733\n",
"Epoch 6/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.0603 - accuracy: 0.9831 - val_loss: 0.0877 - val_accuracy: 0.9745\n",
"Epoch 7/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.0503 - accuracy: 0.9865 - val_loss: 0.0842 - val_accuracy: 0.9757\n",
"Epoch 8/10\n",
"469/469 [==============================] - 4s 8ms/step - loss: 0.0422 - accuracy: 0.9891 - val_loss: 0.0816 - val_accuracy: 0.9756\n",
"Epoch 9/10\n",
"469/469 [==============================] - 4s 9ms/step - loss: 0.0353 - accuracy: 0.9913 - val_loss: 0.0803 - val_accuracy: 0.9758\n",
"Epoch 10/10\n",
"469/469 [==============================] - 4s 9ms/step - loss: 0.0295 - accuracy: 0.9930 - val_loss: 0.0786 - val_accuracy: 0.9763\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f1769bb0c50>"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Td_as7ZCQif8",
"outputId": "c65ce3fe-9745-429b-a45e-323af1475d35"
},
"source": [
"model.save('MNIST-Model')"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: MNIST-Model/assets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: MNIST-Model/assets\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iAMyWSpsQn2G",
"outputId": "c594ef3f-b904-4c2c-aa12-38fd10e34ac3"
},
"source": [
"!zip -r /content/MNIST.zip /content/MNIST-Model"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
" adding: content/MNIST-Model/ (stored 0%)\n",
" adding: content/MNIST-Model/assets/ (stored 0%)\n",
" adding: content/MNIST-Model/variables/ (stored 0%)\n",
" adding: content/MNIST-Model/variables/variables.data-00000-of-00001 (deflated 12%)\n",
" adding: content/MNIST-Model/variables/variables.index (deflated 59%)\n",
" adding: content/MNIST-Model/saved_model.pb (deflated 87%)\n",
" adding: content/MNIST-Model/keras_metadata.pb (deflated 85%)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "ka7I7rE9Qv-o",
"outputId": "a4e7a9f4-2489-4f85-ed8a-1237c4036094"
},
"source": [
"from google.colab import files\n",
"files.download('/content/MNIST.zip')"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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},
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},
{
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