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ModelZoo
ResNet50_tensorflow
Commits
3d540429
Unverified
Commit
3d540429
authored
Jun 19, 2018
by
Mark Daoust
Committed by
GitHub
Jun 19, 2018
Browse files
Merge pull request #4585 from MarkDaoust/no-string-activations
Remove string activations + typos.
parents
dfd045e5
39f9e609
Changes
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4 changed files
with
124 additions
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574 deletions
+124
-574
samples/core/get_started/basic_classification.ipynb
samples/core/get_started/basic_classification.ipynb
+44
-170
samples/core/get_started/basic_regression.ipynb
samples/core/get_started/basic_regression.ipynb
+26
-103
samples/core/get_started/basic_text_classification.ipynb
samples/core/get_started/basic_text_classification.ipynb
+23
-127
samples/core/get_started/custom_training_walkthrough.ipynb
samples/core/get_started/custom_training_walkthrough.ipynb
+31
-174
No files found.
samples/core/get_started/basic_classification.ipynb
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"colab": {
"name": "basic_classification.ipynb",
"version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [],
"private_outputs": true,
"collapsed_sections": [],
...
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@@ -32,13 +30,7 @@
"metadata": {
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"source": [
...
...
@@ -61,13 +53,7 @@
"metadata": {
"id": "vasWnqRgy1H4",
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"cell_type": "code",
"source": [
...
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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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...
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@@ -198,12 +179,7 @@
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"colab": {
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"source": [
...
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@@ -282,12 +258,7 @@
"metadata": {
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@@ -313,12 +284,7 @@
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@@ -341,12 +307,7 @@
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@@ -369,12 +330,7 @@
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"source": [
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@@ -397,12 +353,7 @@
"metadata": {
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"metadata": {
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@@ -455,12 +401,7 @@
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@@ -486,12 +427,7 @@
"metadata": {
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"source": [
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@@ -517,12 +453,7 @@
"metadata": {
"id": "bW5WzIPlCaWv",
"colab_type": "code",
"colab": {
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"source": [
...
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@@ -547,12 +478,7 @@
"metadata": {
"id": "oZTImqg_CaW1",
"colab_type": "code",
"colab": {
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"source": [
...
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@@ -601,19 +527,14 @@
"metadata": {
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"colab": {
"autoexec": {
"startup": false,
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},
"cell_type": "code",
"source": [
"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",
"])"
],
"execution_count": 0,
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@@ -643,12 +564,7 @@
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"colab": {}
},
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"source": [
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@@ -681,12 +597,7 @@
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"source": [
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"metadata": {
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@@ -847,12 +738,7 @@
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"source": [
...
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@@ -875,12 +761,7 @@
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"source": [
...
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@@ -921,12 +802,7 @@
"metadata": {
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"colab_type": "code",
"colab": {
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"colab": {}
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"source": [
...
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@@ -952,12 +828,7 @@
"metadata": {
"id": "lDFh5yF_CaXW",
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"colab": {
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}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -983,12 +854,7 @@
"metadata": {
"id": "o_rzNSdrCaXY",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -1013,12 +879,7 @@
"metadata": {
"id": "2tRmdq_8CaXb",
"colab_type": "code",
"colab": {
"autoexec": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -1038,6 +899,19 @@
"source": [
"And, as before, the model predicts a label of 9."
]
},
{
"metadata": {
"id": "fzHAx2M99WCd",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
\ No newline at end of file
samples/core/get_started/basic_regression.ipynb
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"colab": {
"name": "basic-regression.ipynb",
"version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [],
"private_outputs": true,
"collapsed_sections": [
...
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@@ -34,13 +32,7 @@
"metadata": {
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"wait_interval": 0
}
},
"cellView": "form"
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -63,13 +55,7 @@
"metadata": {
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"colab": {
"autoexec": {
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"wait_interval": 0
}
},
"cellView": "form"
"colab": {}
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"cell_type": "code",
"source": [
...
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@@ -141,12 +127,7 @@
"metadata": {
"id": "1rRo8oNqZ-Rj",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -176,12 +157,7 @@
"metadata": {
"id": "p9kxxgzvzlyz",
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"colab": {
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"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -213,12 +189,7 @@
"metadata": {
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"source": [
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@@ -258,12 +229,7 @@
"metadata": {
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"source": [
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@@ -286,12 +252,7 @@
"metadata": {
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"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
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"metadata": {
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"colab": {
"autoexec": {
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"cell_type": "code",
"source": [
...
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"metadata": {
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"colab_type": "code",
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"cell_type": "code",
"source": [
...
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@@ -399,22 +350,17 @@
"metadata": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
"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 @@
"metadata": {
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}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
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@@ -484,12 +425,7 @@
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"autoexec": {
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"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -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 @@
"metadata": {
"id": "fdMZuhUgzMZ4",
"colab_type": "code",
"colab": {
"autoexec": {
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}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -565,12 +498,7 @@
"metadata": {
"id": "jl_yNr5n1kms",
"colab_type": "code",
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}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -597,12 +525,7 @@
"metadata": {
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}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -622,7 +545,7 @@
"source": [
"## Conclusion\n",
"\n",
"This notebook i a few techniques to
introduc
e a regresson problem.\n",
"This notebook i
ntroduced
a few techniques to
handl
e 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
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@@ -5,8 +5,6 @@
"colab": {
"name": "basic-text-classification.ipynb",
"version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [],
"private_outputs": true,
"collapsed_sections": [],
...
...
@@ -32,13 +30,7 @@
"metadata": {
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},
"cell_type": "code",
"source": [
...
...
@@ -61,13 +53,7 @@
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...
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@@ -139,12 +125,7 @@
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...
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@@ -176,12 +157,7 @@
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...
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@@ -218,12 +194,7 @@
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"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -419,12 +365,7 @@
"metadata": {
"id": "USSSBnkE-lky",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -447,12 +388,7 @@
"metadata": {
"id": "TG8X9cqi-lk9",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -482,12 +418,7 @@
"metadata": {
"id": "xpKOoWgu-llD",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -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 @@
"metadata": {
"id": "Mr0GP-cQ-llN",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
"model.compile(optimizer=
'adam'
,\n",
"model.compile(optimizer=
tf.train.AdamOptimizer()
,\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy'])"
],
...
...
@@ -588,12 +514,7 @@
"metadata": {
"id": "-NpcXY9--llS",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -622,12 +543,7 @@
"metadata": {
"id": "tXSGrjWZ-llW",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -657,12 +573,7 @@
"metadata": {
"id": "zOMKywn4zReN",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -699,12 +610,7 @@
"metadata": {
"id": "VcvSXvhp-llb",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -728,12 +634,7 @@
"metadata": {
"id": "nGoYf2Js-lle",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -764,12 +665,7 @@
"metadata": {
"id": "6hXx-xOv-llh",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"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",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"cellView": "form"
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -145,12 +137,7 @@
"metadata": {
"id": "jBmKxLVy9Uhg",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -177,12 +164,7 @@
"metadata": {
"id": "g4Wzg69bnwK2",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -252,12 +234,7 @@
"metadata": {
"id": "J6c7uEU9rjRM",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -287,12 +264,7 @@
"metadata": {
"id": "FQvb_JYdrpPm",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -323,12 +295,7 @@
"metadata": {
"id": "9Edhevw7exl6",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -364,12 +331,7 @@
"metadata": {
"id": "sVNlJlUOhkoX",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -397,12 +359,7 @@
"metadata": {
"id": "WsxHnz1ebJ2S",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -434,12 +391,7 @@
"metadata": {
"id": "iDuG94H-C122",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -466,12 +418,7 @@
"metadata": {
"id": "me5Wn-9FcyyO",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -502,12 +449,7 @@
"metadata": {
"id": "jm932WINcaGU",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -533,12 +475,7 @@
"metadata": {
"id": "ZbDkzGZIkpXf",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -561,12 +498,7 @@
"metadata": {
"id": "kex9ibEek6Tr",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -627,18 +559,13 @@
"metadata": {
"id": "2fZ6oL2ig3ZK",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"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": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -704,12 +626,7 @@
"metadata": {
"id": "_tRwHZmTNTX2",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -732,12 +649,7 @@
"metadata": {
"id": "-Jzm_GoErz8B",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -779,12 +691,7 @@
"metadata": {
"id": "tMAT4DcMPwI-",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -813,12 +720,7 @@
"metadata": {
"id": "x57HcKWhKkei",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -868,12 +770,7 @@
"metadata": {
"id": "8xxi2NNGKwG_",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -898,12 +795,7 @@
"metadata": {
"id": "rxRNTFVe56RG",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -945,12 +837,7 @@
"metadata": {
"id": "AIgulGRUhpto",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -1016,12 +903,7 @@
"metadata": {
"id": "agjvNd2iUGFn",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -1101,12 +983,7 @@
"metadata": {
"id": "Ps3_9dJ3Lodk",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"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": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
@@ -1231,12 +1093,7 @@
"metadata": {
"id": "kesTS5Lzv-M2",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
"colab": {}
},
"cell_type": "code",
"source": [
...
...
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