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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
Hide whitespace changes
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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 @@
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......@@ -32,13 +30,7 @@
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......@@ -61,13 +53,7 @@
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......@@ -114,10 +100,10 @@
"cell_type": "markdown",
"source": [
"<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",
"</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"
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......@@ -136,12 +122,7 @@
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......@@ -313,12 +284,7 @@
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......@@ -369,12 +330,7 @@
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......@@ -425,12 +376,7 @@
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......@@ -547,12 +478,7 @@
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......@@ -601,19 +527,14 @@
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"model = keras.Sequential([\n",
" keras.layers.Flatten(input_shape=(28, 28)),\n",
" keras.layers.Dense(128, activation='relu'),\n",
" keras.layers.Dense(10, activation='softmax')\n",
" keras.layers.Dense(128, activation=tf.nn.relu),\n",
" keras.layers.Dense(10, activation=tf.nn.softmax)\n",
"])"
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......@@ -643,12 +564,7 @@
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......@@ -983,12 +854,7 @@
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......@@ -1013,12 +879,7 @@
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......@@ -1038,6 +899,19 @@
"source": [
"And, as before, the model predicts a label of 9."
]
},
{
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""
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]
}
\ No newline at end of file
samples/core/get_started/basic_regression.ipynb
View file @ 3d540429
......@@ -5,8 +5,6 @@
"colab": {
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......@@ -34,13 +32,7 @@
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......@@ -63,13 +55,7 @@
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......@@ -141,12 +127,7 @@
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......@@ -176,12 +157,7 @@
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......@@ -213,12 +189,7 @@
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......@@ -286,12 +252,7 @@
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......@@ -322,12 +283,7 @@
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......@@ -352,12 +308,7 @@
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......@@ -399,22 +350,17 @@
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"def build_model():\n",
" model = keras.Sequential()\n",
" \n",
" model.add(keras.layers.Dense(64, activation=tf.nn.relu,\n",
" input_shape=(train_data.shape[1],)))\n",
" model.add(keras.layers.Dense(64, activation=tf.nn.relu))\n",
" model.add(keras.layers.Dense(1))\n",
" model = keras.Sequential([\n",
" keras.layers.Dense(64, activation=tf.nn.relu, \n",
" input_shape=(train_data.shape[1],)),\n",
" keras.layers.Dense(64, activation=tf.nn.relu),\n",
" keras.layers.Dense(1)\n",
" ])\n",
"\n",
" optimizer = tf.train.RMSPropOptimizer(0.001)\n",
"\n",
......@@ -445,12 +391,7 @@
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......@@ -500,8 +436,10 @@
" plt.figure()\n",
" plt.xlabel('Epoch')\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['val_mean_absolute_error']), label = 'Val loss')\n",
" plt.plot(history.epoch, np.array(history.history['mean_absolute_error']), \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.ylim([0,5])\n",
"\n",
......@@ -526,12 +464,7 @@
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......@@ -597,12 +525,7 @@
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......@@ -622,7 +545,7 @@
"source": [
"## Conclusion\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",
"* 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",
......
samples/core/get_started/basic_text_classification.ipynb
View file @ 3d540429
......@@ -5,8 +5,6 @@
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......@@ -32,13 +30,7 @@
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......@@ -61,13 +53,7 @@
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......@@ -139,12 +125,7 @@
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......@@ -176,12 +157,7 @@
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......@@ -304,12 +265,7 @@
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......@@ -383,12 +334,7 @@
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......@@ -419,12 +365,7 @@
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......@@ -497,8 +428,8 @@
"model = keras.Sequential()\n",
"model.add(keras.layers.Embedding(vocab_size, 16))\n",
"model.add(keras.layers.GlobalAveragePooling1D())\n",
"model.add(keras.layers.Dense(16, activation='relu'))\n",
"model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
"model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n",
"model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
"\n",
"model.summary()"
],
......@@ -556,16 +487,11 @@
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"source": [
"model.compile(optimizer='adam',\n",
"model.compile(optimizer=tf.train.AdamOptimizer(),\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy'])"
],
......@@ -588,12 +514,7 @@
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"colab": {
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"colab": {}
},
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"source": [
......@@ -622,12 +543,7 @@
"metadata": {
"id": "tXSGrjWZ-llW",
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"colab": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -657,12 +573,7 @@
"metadata": {
"id": "zOMKywn4zReN",
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"colab": {
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"colab": {}
},
"cell_type": "code",
"source": [
......@@ -699,12 +610,7 @@
"metadata": {
"id": "VcvSXvhp-llb",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -728,12 +634,7 @@
"metadata": {
"id": "nGoYf2Js-lle",
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"colab": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -764,12 +665,7 @@
"metadata": {
"id": "6hXx-xOv-llh",
"colab_type": "code",
"colab": {
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"colab": {}
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"cell_type": "code",
"source": [
......
samples/core/get_started/custom_training_walkthrough.ipynb
View file @ 3d540429
......@@ -5,8 +5,6 @@
"colab": {
"name": "custom-training-walkthrough.ipynb",
"version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [],
"private_outputs": true,
"collapsed_sections": [],
......@@ -34,13 +32,7 @@
"metadata": {
"id": "CPII1rGR2rF9",
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},
"cellView": "form"
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -145,12 +137,7 @@
"metadata": {
"id": "jBmKxLVy9Uhg",
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}
"colab": {}
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"source": [
......@@ -177,12 +164,7 @@
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"colab": {}
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"cell_type": "code",
"source": [
......@@ -252,12 +234,7 @@
"metadata": {
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"colab": {}
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"source": [
......@@ -287,12 +264,7 @@
"metadata": {
"id": "FQvb_JYdrpPm",
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"colab": {}
},
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"source": [
......@@ -323,12 +295,7 @@
"metadata": {
"id": "9Edhevw7exl6",
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"colab": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -364,12 +331,7 @@
"metadata": {
"id": "sVNlJlUOhkoX",
"colab_type": "code",
"colab": {
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}
"colab": {}
},
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"source": [
......@@ -397,12 +359,7 @@
"metadata": {
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"colab": {}
},
"cell_type": "code",
"source": [
......@@ -434,12 +391,7 @@
"metadata": {
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"colab": {}
},
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"source": [
......@@ -466,12 +418,7 @@
"metadata": {
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"colab": {
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"colab": {}
},
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"source": [
......@@ -502,12 +449,7 @@
"metadata": {
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"colab": {}
},
"cell_type": "code",
"source": [
......@@ -533,12 +475,7 @@
"metadata": {
"id": "ZbDkzGZIkpXf",
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}
"colab": {}
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"source": [
......@@ -561,12 +498,7 @@
"metadata": {
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"source": [
......@@ -627,18 +559,13 @@
"metadata": {
"id": "2fZ6oL2ig3ZK",
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},
"cell_type": "code",
"source": [
"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=\"relu\"),\n",
" tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)), # input shape required\n",
" tf.keras.layers.Dense(10, activation=tf.nn.relu),\n",
" tf.keras.layers.Dense(3)\n",
"])"
],
......@@ -673,12 +600,7 @@
"metadata": {
"id": "xe6SQ5NrpB-I",
"colab_type": "code",
"colab": {
"autoexec": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -704,12 +626,7 @@
"metadata": {
"id": "_tRwHZmTNTX2",
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"source": [
......@@ -732,12 +649,7 @@
"metadata": {
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"colab": {}
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"source": [
......@@ -779,12 +691,7 @@
"metadata": {
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"source": [
......@@ -813,12 +720,7 @@
"metadata": {
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}
"colab": {}
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"source": [
......@@ -868,12 +770,7 @@
"metadata": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -898,12 +795,7 @@
"metadata": {
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"source": [
......@@ -945,12 +837,7 @@
"metadata": {
"id": "AIgulGRUhpto",
"colab_type": "code",
"colab": {
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"colab": {}
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"source": [
......@@ -1016,12 +903,7 @@
"metadata": {
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"source": [
......@@ -1101,12 +983,7 @@
"metadata": {
"id": "Ps3_9dJ3Lodk",
"colab_type": "code",
"colab": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -1122,12 +999,7 @@
"metadata": {
"id": "SRMWCu30bnxH",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -1160,12 +1032,7 @@
"metadata": {
"id": "Tw03-MK1cYId",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -1195,12 +1062,7 @@
"metadata": {
"id": "uNwt2eMeOane",
"colab_type": "code",
"colab": {
"autoexec": {
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"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -1231,12 +1093,7 @@
"metadata": {
"id": "kesTS5Lzv-M2",
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}
}
"colab": {}
},
"cell_type": "code",
"source": [
......@@ -1282,4 +1139,4 @@
]
}
]
}
\ No newline at end of file
}
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