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"library/src/utility/host_tensor.cpp" did not exist on "71d6b19d18e267bb6b8e04711bc37e241aaed55e"
Unverified Commit 3d540429 authored by Mark Daoust's avatar Mark Daoust Committed by GitHub
Browse files

Merge pull request #4585 from MarkDaoust/no-string-activations 

Remove string activations + typos.
parents dfd045e5 39f9e609
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Showing with 124 additions and 574 deletions
samples/core/get_started/basic_classification.ipynb
View file @ 3d540429
...@@ -5,8 +5,6 @@ ...@@ -5,8 +5,6 @@
"colab": { "colab": {
"name": "basic_classification.ipynb", "name": "basic_classification.ipynb",
"version": "0.3.2", "version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [], "provenance": [],
"private_outputs": true, "private_outputs": true,
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...@@ -32,13 +30,7 @@ ...@@ -32,13 +30,7 @@
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"colab_type": "code", "colab_type": "code",
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...@@ -61,13 +53,7 @@ ...@@ -61,13 +53,7 @@
"metadata": { "metadata": {
"id": "vasWnqRgy1H4", "id": "vasWnqRgy1H4",
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"source": [ "source": [
...@@ -114,10 +100,10 @@ ...@@ -114,10 +100,10 @@
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"<table align=\"left\"><td>\n", "<table align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.sandbox.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic-classification.ipynb\">\n", "<a target=\"_blank\" href=\"https://colab.sandbox.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n", "</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/samples/core/get_started/basic-classification.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on Github</a></td></table>\n" "<a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on Github</a></td></table>\n"
] ]
}, },
{ {
...@@ -136,12 +122,7 @@ ...@@ -136,12 +122,7 @@
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...@@ -198,12 +179,7 @@ ...@@ -198,12 +179,7 @@
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...@@ -282,12 +258,7 @@ ...@@ -282,12 +258,7 @@
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...@@ -313,12 +284,7 @@ ...@@ -313,12 +284,7 @@
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...@@ -341,12 +307,7 @@ ...@@ -341,12 +307,7 @@
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...@@ -369,12 +330,7 @@ ...@@ -369,12 +330,7 @@
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...@@ -397,12 +353,7 @@ ...@@ -397,12 +353,7 @@
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...@@ -425,12 +376,7 @@ ...@@ -425,12 +376,7 @@
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...@@ -455,12 +401,7 @@ ...@@ -455,12 +401,7 @@
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...@@ -486,12 +427,7 @@ ...@@ -486,12 +427,7 @@
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...@@ -517,12 +453,7 @@ ...@@ -517,12 +453,7 @@
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...@@ -547,12 +478,7 @@ ...@@ -547,12 +478,7 @@
"metadata": { "metadata": {
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...@@ -601,19 +527,14 @@ ...@@ -601,19 +527,14 @@
"metadata": { "metadata": {
"id": "9ODch-OFCaW4", "id": "9ODch-OFCaW4",
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"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"model = keras.Sequential([\n", "model = keras.Sequential([\n",
" keras.layers.Flatten(input_shape=(28, 28)),\n", " keras.layers.Flatten(input_shape=(28, 28)),\n",
" keras.layers.Dense(128, activation='relu'),\n", " keras.layers.Dense(128, activation=tf.nn.relu),\n",
" keras.layers.Dense(10, activation='softmax')\n", " keras.layers.Dense(10, activation=tf.nn.softmax)\n",
"])" "])"
], ],
"execution_count": 0, "execution_count": 0,
...@@ -643,12 +564,7 @@ ...@@ -643,12 +564,7 @@
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...@@ -681,12 +597,7 @@ ...@@ -681,12 +597,7 @@
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"id": "xvwvpA64CaW_", "id": "xvwvpA64CaW_",
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...@@ -721,12 +632,7 @@ ...@@ -721,12 +632,7 @@
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...@@ -763,12 +669,7 @@ ...@@ -763,12 +669,7 @@
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...@@ -791,12 +692,7 @@ ...@@ -791,12 +692,7 @@
"metadata": { "metadata": {
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...@@ -819,12 +715,7 @@ ...@@ -819,12 +715,7 @@
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...@@ -847,12 +738,7 @@ ...@@ -847,12 +738,7 @@
"metadata": { "metadata": {
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...@@ -875,12 +761,7 @@ ...@@ -875,12 +761,7 @@
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...@@ -921,12 +802,7 @@ ...@@ -921,12 +802,7 @@
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...@@ -952,12 +828,7 @@ ...@@ -952,12 +828,7 @@
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...@@ -983,12 +854,7 @@ ...@@ -983,12 +854,7 @@
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...@@ -1013,12 +879,7 @@ ...@@ -1013,12 +879,7 @@
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...@@ -1038,6 +899,19 @@ ...@@ -1038,6 +899,19 @@
"source": [ "source": [
"And, as before, the model predicts a label of 9." "And, as before, the model predicts a label of 9."
] ]
},
{
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},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
} }
] ]
} }
\ No newline at end of file
samples/core/get_started/basic_regression.ipynb
View file @ 3d540429
...@@ -5,8 +5,6 @@ ...@@ -5,8 +5,6 @@
"colab": { "colab": {
"name": "basic-regression.ipynb", "name": "basic-regression.ipynb",
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"views": {},
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...@@ -34,13 +32,7 @@ ...@@ -34,13 +32,7 @@
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...@@ -63,13 +55,7 @@ ...@@ -63,13 +55,7 @@
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...@@ -141,12 +127,7 @@ ...@@ -141,12 +127,7 @@
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...@@ -176,12 +157,7 @@ ...@@ -176,12 +157,7 @@
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...@@ -213,12 +189,7 @@ ...@@ -213,12 +189,7 @@
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...@@ -258,12 +229,7 @@ ...@@ -258,12 +229,7 @@
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...@@ -286,12 +252,7 @@ ...@@ -286,12 +252,7 @@
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...@@ -322,12 +283,7 @@ ...@@ -322,12 +283,7 @@
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...@@ -352,12 +308,7 @@ ...@@ -352,12 +308,7 @@
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...@@ -399,22 +350,17 @@ ...@@ -399,22 +350,17 @@
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"source": [ "source": [
"def build_model():\n", "def build_model():\n",
" model = keras.Sequential()\n", " model = keras.Sequential([\n",
" \n", " keras.layers.Dense(64, activation=tf.nn.relu, \n",
" model.add(keras.layers.Dense(64, activation=tf.nn.relu,\n", " input_shape=(train_data.shape[1],)),\n",
" input_shape=(train_data.shape[1],)))\n", " keras.layers.Dense(64, activation=tf.nn.relu),\n",
" model.add(keras.layers.Dense(64, activation=tf.nn.relu))\n", " keras.layers.Dense(1)\n",
" model.add(keras.layers.Dense(1))\n", " ])\n",
"\n", "\n",
" optimizer = tf.train.RMSPropOptimizer(0.001)\n", " optimizer = tf.train.RMSPropOptimizer(0.001)\n",
"\n", "\n",
...@@ -445,12 +391,7 @@ ...@@ -445,12 +391,7 @@
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...@@ -484,12 +425,7 @@ ...@@ -484,12 +425,7 @@
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...@@ -500,8 +436,10 @@ ...@@ -500,8 +436,10 @@
" plt.figure()\n", " plt.figure()\n",
" plt.xlabel('Epoch')\n", " plt.xlabel('Epoch')\n",
" plt.ylabel('Mean Abs Error [1000$]')\n", " plt.ylabel('Mean Abs Error [1000$]')\n",
" plt.plot(history.epoch, np.array(history.history['mean_absolute_error']), label='Train Loss')\n", " plt.plot(history.epoch, np.array(history.history['mean_absolute_error']), \n",
" plt.plot(history.epoch, np.array(history.history['val_mean_absolute_error']), label = 'Val loss')\n", " label='Train Loss')\n",
" plt.plot(history.epoch, np.array(history.history['val_mean_absolute_error']),\n",
" label = 'Val loss')\n",
" plt.legend()\n", " plt.legend()\n",
" plt.ylim([0,5])\n", " plt.ylim([0,5])\n",
"\n", "\n",
...@@ -526,12 +464,7 @@ ...@@ -526,12 +464,7 @@
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...@@ -565,12 +498,7 @@ ...@@ -565,12 +498,7 @@
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...@@ -597,12 +525,7 @@ ...@@ -597,12 +525,7 @@
"metadata": { "metadata": {
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"source": [ "source": [
...@@ -622,7 +545,7 @@ ...@@ -622,7 +545,7 @@
"source": [ "source": [
"## Conclusion\n", "## Conclusion\n",
"\n", "\n",
"This notebook i a few techniques to introduce a regresson problem.\n", "This notebook introduced a few techniques to handle a regresson problem.\n",
"\n", "\n",
"* Mean Squared Error (MSE) is a common loss function used for regression problems (different than classification problems).\n", "* Mean Squared Error (MSE) is a common loss function used for regression problems (different than classification problems).\n",
"* Similarly, evaluation metrics used for regression differ from classification. A common regression metric is Mean Absolute Error (MAE).\n", "* Similarly, evaluation metrics used for regression differ from classification. A common regression metric is Mean Absolute Error (MAE).\n",
......
samples/core/get_started/basic_text_classification.ipynb
View file @ 3d540429
...@@ -5,8 +5,6 @@ ...@@ -5,8 +5,6 @@
"colab": { "colab": {
"name": "basic-text-classification.ipynb", "name": "basic-text-classification.ipynb",
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...@@ -32,13 +30,7 @@ ...@@ -32,13 +30,7 @@
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...@@ -61,13 +53,7 @@ ...@@ -61,13 +53,7 @@
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...@@ -139,12 +125,7 @@ ...@@ -139,12 +125,7 @@
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...@@ -176,12 +157,7 @@ ...@@ -176,12 +157,7 @@
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...@@ -218,12 +194,7 @@ ...@@ -218,12 +194,7 @@
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...@@ -246,12 +217,7 @@ ...@@ -246,12 +217,7 @@
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...@@ -274,12 +240,7 @@ ...@@ -274,12 +240,7 @@
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...@@ -345,12 +301,7 @@ ...@@ -345,12 +301,7 @@
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...@@ -447,12 +388,7 @@ ...@@ -447,12 +388,7 @@
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...@@ -497,8 +428,8 @@ ...@@ -497,8 +428,8 @@
"model = keras.Sequential()\n", "model = keras.Sequential()\n",
"model.add(keras.layers.Embedding(vocab_size, 16))\n", "model.add(keras.layers.Embedding(vocab_size, 16))\n",
"model.add(keras.layers.GlobalAveragePooling1D())\n", "model.add(keras.layers.GlobalAveragePooling1D())\n",
"model.add(keras.layers.Dense(16, activation='relu'))\n", "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n",
"model.add(keras.layers.Dense(1, activation='sigmoid'))\n", "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
"\n", "\n",
"model.summary()" "model.summary()"
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...@@ -556,16 +487,11 @@ ...@@ -556,16 +487,11 @@
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"model.compile(optimizer='adam',\n", "model.compile(optimizer=tf.train.AdamOptimizer(),\n",
" loss='binary_crossentropy',\n", " loss='binary_crossentropy',\n",
" metrics=['accuracy'])" " metrics=['accuracy'])"
], ],
...@@ -588,12 +514,7 @@ ...@@ -588,12 +514,7 @@
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...@@ -657,12 +573,7 @@ ...@@ -657,12 +573,7 @@
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...@@ -764,12 +665,7 @@ ...@@ -764,12 +665,7 @@
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......
samples/core/get_started/custom_training_walkthrough.ipynb
View file @ 3d540429
...@@ -5,8 +5,6 @@ ...@@ -5,8 +5,6 @@
"colab": { "colab": {
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...@@ -34,13 +32,7 @@ ...@@ -34,13 +32,7 @@
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...@@ -145,12 +137,7 @@ ...@@ -145,12 +137,7 @@
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...@@ -177,12 +164,7 @@ ...@@ -177,12 +164,7 @@
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...@@ -252,12 +234,7 @@ ...@@ -252,12 +234,7 @@
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...@@ -287,12 +264,7 @@ ...@@ -287,12 +264,7 @@
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...@@ -323,12 +295,7 @@ ...@@ -323,12 +295,7 @@
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...@@ -364,12 +331,7 @@ ...@@ -364,12 +331,7 @@
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...@@ -397,12 +359,7 @@ ...@@ -397,12 +359,7 @@
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...@@ -434,12 +391,7 @@ ...@@ -434,12 +391,7 @@
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...@@ -466,12 +418,7 @@ ...@@ -466,12 +418,7 @@
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...@@ -502,12 +449,7 @@ ...@@ -502,12 +449,7 @@
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...@@ -561,12 +498,7 @@ ...@@ -561,12 +498,7 @@
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...@@ -627,18 +559,13 @@ ...@@ -627,18 +559,13 @@
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"model = tf.keras.Sequential([\n", "model = tf.keras.Sequential([\n",
" tf.keras.layers.Dense(10, activation=\"relu\", input_shape=(4,)), # input shape required\n", " tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)), # input shape required\n",
" tf.keras.layers.Dense(10, activation=\"relu\"),\n", " tf.keras.layers.Dense(10, activation=tf.nn.relu),\n",
" tf.keras.layers.Dense(3)\n", " tf.keras.layers.Dense(3)\n",
"])" "])"
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...@@ -673,12 +600,7 @@ ...@@ -673,12 +600,7 @@
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...@@ -1016,12 +903,7 @@ ...@@ -1016,12 +903,7 @@
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"id": "agjvNd2iUGFn", "id": "agjvNd2iUGFn",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
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...@@ -1101,12 +983,7 @@ ...@@ -1101,12 +983,7 @@
"metadata": { "metadata": {
"id": "Ps3_9dJ3Lodk", "id": "Ps3_9dJ3Lodk",
"colab_type": "code", "colab_type": "code",
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"cell_type": "code", "cell_type": "code",
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...@@ -1122,12 +999,7 @@ ...@@ -1122,12 +999,7 @@
"metadata": { "metadata": {
"id": "SRMWCu30bnxH", "id": "SRMWCu30bnxH",
"colab_type": "code", "colab_type": "code",
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"cell_type": "code", "cell_type": "code",
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...@@ -1160,12 +1032,7 @@ ...@@ -1160,12 +1032,7 @@
"metadata": { "metadata": {
"id": "Tw03-MK1cYId", "id": "Tw03-MK1cYId",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
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}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
...@@ -1195,12 +1062,7 @@ ...@@ -1195,12 +1062,7 @@
"metadata": { "metadata": {
"id": "uNwt2eMeOane", "id": "uNwt2eMeOane",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
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}, },
"cell_type": "code", "cell_type": "code",
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...@@ -1231,12 +1093,7 @@ ...@@ -1231,12 +1093,7 @@
"metadata": { "metadata": {
"id": "kesTS5Lzv-M2", "id": "kesTS5Lzv-M2",
"colab_type": "code", "colab_type": "code",
"colab": { "colab": {}
"autoexec": {
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"wait_interval": 0
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}
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
......
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