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merged 2 commits into from
Mar 25, 2022
Merged

add --profile=black to isort args #1702

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bba72a2
add --profile=black to isort args
LukeWood Mar 25, 2022
59440cb
Reformat impacted notebooks
LukeWood Mar 25, 2022
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16 changes: 6 additions & 10 deletions docs/ipynb/customized.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
},
"outputs": [],
"source": [
"!pip install autokeras\n"
"!pip install autokeras"
]
},
{
Expand All @@ -23,7 +23,7 @@
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import mnist\n",
"\n",
"import autokeras as ak\n"
"import autokeras as ak"
]
},
{
Expand Down Expand Up @@ -67,7 +67,6 @@
},
"outputs": [],
"source": [
"\n",
"input_node = ak.ImageInput()\n",
"output_node = ak.Normalization()(input_node)\n",
"output_node1 = ak.ConvBlock()(output_node)\n",
Expand All @@ -77,7 +76,7 @@
"\n",
"auto_model = ak.AutoModel(\n",
" inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
")\n"
")"
]
},
{
Expand Down Expand Up @@ -108,7 +107,6 @@
},
"outputs": [],
"source": [
"\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"print(x_train.shape) # (60000, 28, 28)\n",
"print(y_train.shape) # (60000,)\n",
Expand All @@ -119,7 +117,7 @@
"# Predict with the best model.\n",
"predicted_y = auto_model.predict(x_test)\n",
"# Evaluate the best model with testing data.\n",
"print(auto_model.evaluate(x_test, y_test))\n"
"print(auto_model.evaluate(x_test, y_test))"
]
},
{
Expand Down Expand Up @@ -176,7 +174,6 @@
},
"outputs": [],
"source": [
"\n",
"class SingleDenseLayerBlock(ak.Block):\n",
" def build(self, hp, inputs=None):\n",
" # Get the input_node from inputs.\n",
Expand All @@ -185,8 +182,7 @@
" hp.Int(\"num_units\", min_value=32, max_value=512, step=32)\n",
" )\n",
" output_node = layer(input_node)\n",
" return output_node\n",
"\n"
" return output_node"
]
},
{
Expand Down Expand Up @@ -220,7 +216,7 @@
"y_test = np.random.rand(num_instances, 1).astype(np.float32)\n",
"# Train the model\n",
"auto_model.fit(x_train, y_train, epochs=1)\n",
"print(auto_model.evaluate(x_test, y_test))\n"
"print(auto_model.evaluate(x_test, y_test))"
]
},
{
Expand Down
7 changes: 3 additions & 4 deletions docs/ipynb/export.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
},
"outputs": [],
"source": [
"!pip install autokeras\n"
"!pip install autokeras"
]
},
{
Expand All @@ -23,7 +23,7 @@
"from tensorflow.keras.datasets import mnist\n",
"from tensorflow.keras.models import load_model\n",
"\n",
"import autokeras as ak\n"
"import autokeras as ak"
]
},
{
Expand All @@ -47,7 +47,6 @@
},
"outputs": [],
"source": [
"\n",
"print(tf.__version__)\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
Expand All @@ -71,7 +70,7 @@
"loaded_model = load_model(\"model_autokeras\", custom_objects=ak.CUSTOM_OBJECTS)\n",
"\n",
"predicted_y = loaded_model.predict(tf.expand_dims(x_test, -1))\n",
"print(predicted_y)\n"
"print(predicted_y)"
]
}
],
Expand Down
20 changes: 10 additions & 10 deletions docs/ipynb/image_classification.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
},
"outputs": [],
"source": [
"!pip install autokeras\n"
"!pip install autokeras"
]
},
{
Expand All @@ -23,7 +23,7 @@
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import mnist\n",
"\n",
"import autokeras as ak\n"
"import autokeras as ak"
]
},
{
Expand All @@ -47,7 +47,7 @@
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"print(x_train.shape) # (60000, 28, 28)\n",
"print(y_train.shape) # (60000,)\n",
"print(y_train[:3]) # array([7, 2, 1], dtype=uint8)\n"
"print(y_train[:3]) # array([7, 2, 1], dtype=uint8)"
]
},
{
Expand Down Expand Up @@ -83,7 +83,7 @@
"\n",
"\n",
"# Evaluate the best model with testing data.\n",
"print(clf.evaluate(x_test, y_test))\n"
"print(clf.evaluate(x_test, y_test))"
]
},
{
Expand Down Expand Up @@ -112,7 +112,7 @@
" # Split the training data and use the last 15% as validation data.\n",
" validation_split=0.15,\n",
" epochs=10,\n",
")\n"
")"
]
},
{
Expand Down Expand Up @@ -144,7 +144,7 @@
" # Use your own validation set.\n",
" validation_data=(x_val, y_val),\n",
" epochs=10,\n",
")\n"
")"
]
},
{
Expand Down Expand Up @@ -184,7 +184,7 @@
"clf = ak.AutoModel(\n",
" inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
")\n",
"clf.fit(x_train, y_train, epochs=10)\n"
"clf.fit(x_train, y_train, epochs=10)"
]
},
{
Expand Down Expand Up @@ -218,7 +218,7 @@
"clf = ak.AutoModel(\n",
" inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
")\n",
"clf.fit(x_train, y_train, epochs=10)\n"
"clf.fit(x_train, y_train, epochs=10)"
]
},
{
Expand Down Expand Up @@ -268,7 +268,7 @@
"print(y_train[:3])\n",
"# array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
"# [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
"# [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])\n"
"# [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])"
]
},
{
Expand Down Expand Up @@ -297,7 +297,7 @@
"# Predict with the best model.\n",
"predicted_y = clf.predict(test_set)\n",
"# Evaluate the best model with testing data.\n",
"print(clf.evaluate(test_set))\n"
"print(clf.evaluate(test_set))"
]
},
{
Expand Down
19 changes: 9 additions & 10 deletions docs/ipynb/image_regression.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
},
"outputs": [],
"source": [
"!pip install autokeras\n"
"!pip install autokeras"
]
},
{
Expand All @@ -22,7 +22,7 @@
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import mnist\n",
"\n",
"import autokeras as ak\n"
"import autokeras as ak"
]
},
{
Expand All @@ -49,13 +49,12 @@
},
"outputs": [],
"source": [
"\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"x_train = x_train[:100]\n",
"y_train = y_train[:100]\n",
"print(x_train.shape) # (60000, 28, 28)\n",
"print(y_train.shape) # (60000,)\n",
"print(y_train[:3]) # array([7, 2, 1], dtype=uint8)\n"
"print(y_train[:3]) # array([7, 2, 1], dtype=uint8)"
]
},
{
Expand Down Expand Up @@ -90,7 +89,7 @@
"\n",
"\n",
"# Evaluate the best model with testing data.\n",
"print(reg.evaluate(x_test, y_test))\n"
"print(reg.evaluate(x_test, y_test))"
]
},
{
Expand Down Expand Up @@ -119,7 +118,7 @@
" # Split the training data and use the last 15% as validation data.\n",
" validation_split=0.15,\n",
" epochs=2,\n",
")\n"
")"
]
},
{
Expand Down Expand Up @@ -151,7 +150,7 @@
" # Use your own validation set.\n",
" validation_data=(x_val, y_val),\n",
" epochs=2,\n",
")\n"
")"
]
},
{
Expand Down Expand Up @@ -191,7 +190,7 @@
"reg = ak.AutoModel(\n",
" inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
")\n",
"reg.fit(x_train, y_train, epochs=2)\n"
"reg.fit(x_train, y_train, epochs=2)"
]
},
{
Expand Down Expand Up @@ -225,7 +224,7 @@
"reg = ak.AutoModel(\n",
" inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
")\n",
"reg.fit(x_train, y_train, epochs=2)\n"
"reg.fit(x_train, y_train, epochs=2)"
]
},
{
Expand Down Expand Up @@ -278,7 +277,7 @@
"# Predict with the best model.\n",
"predicted_y = reg.predict(test_set)\n",
"# Evaluate the best model with testing data.\n",
"print(reg.evaluate(test_set))\n"
"print(reg.evaluate(test_set))"
]
},
{
Expand Down
19 changes: 8 additions & 11 deletions docs/ipynb/load.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
},
"outputs": [],
"source": [
"!pip install autokeras\n"
"!pip install autokeras"
]
},
{
Expand All @@ -25,7 +25,7 @@
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"import autokeras as ak\n"
"import autokeras as ak"
]
},
{
Expand Down Expand Up @@ -61,7 +61,7 @@
"local_dir_path = os.path.dirname(local_file_path)\n",
"# After check mannually, we know the extracted data is in 'flower_photos'.\n",
"data_dir = os.path.join(local_dir_path, \"flower_photos\")\n",
"print(data_dir)\n"
"print(data_dir)"
]
},
{
Expand Down Expand Up @@ -115,7 +115,7 @@
" seed=123,\n",
" image_size=(img_height, img_width),\n",
" batch_size=batch_size,\n",
")\n"
")"
]
},
{
Expand All @@ -137,7 +137,7 @@
"source": [
"clf = ak.ImageClassifier(overwrite=True, max_trials=1)\n",
"clf.fit(train_data, epochs=1)\n",
"print(clf.evaluate(test_data))\n"
"print(clf.evaluate(test_data))"
]
},
{
Expand Down Expand Up @@ -171,8 +171,7 @@
"data_dir = os.path.join(local_dir_path, \"aclImdb\")\n",
"# Remove the unused data folder.\n",
"\n",
"shutil.rmtree(os.path.join(data_dir, \"train/unsup\"))\n",
"\n"
"shutil.rmtree(os.path.join(data_dir, \"train/unsup\"))"
]
},
{
Expand Down Expand Up @@ -204,8 +203,7 @@
"\n",
"clf = ak.TextClassifier(overwrite=True, max_trials=1)\n",
"clf.fit(train_data, epochs=2)\n",
"print(clf.evaluate(test_data))\n",
"\n"
"print(clf.evaluate(test_data))"
]
},
{
Expand All @@ -226,7 +224,6 @@
},
"outputs": [],
"source": [
"\n",
"N_BATCHES = 30\n",
"BATCH_SIZE = 100\n",
"N_FEATURES = 10\n",
Expand Down Expand Up @@ -255,7 +252,7 @@
"\n",
"clf = ak.StructuredDataClassifier(overwrite=True, max_trials=1, seed=5)\n",
"clf.fit(x=dataset, validation_data=dataset, batch_size=BATCH_SIZE)\n",
"print(clf.evaluate(dataset))\n"
"print(clf.evaluate(dataset))"
]
},
{
Expand Down
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