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shallow-training/scratchpad.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
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"id": "physical-coating",
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"tf.get_logger().setLevel('ERROR')\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import seaborn as sns\n",
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"import json\n",
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"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"fig_dpi = 200\n",
"\n",
"%load_ext tensorboard"
]
},
{
"cell_type": "markdown",
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"id": "unable-security",
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"metadata": {},
"source": [
"# Scratchpad\n",
"Testbed"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"id": "precise-invalid",
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"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('features.csv', header=None).T\n",
"data.columns = ['Clump thickness', 'Uniformity of cell size', 'Uniformity of cell shape', 'Marginal adhesion', 'Single epithelial cell size', 'Bare nuclei', 'Bland chomatin', 'Normal nucleoli', 'Mitoses']\n",
"labels = pd.read_csv('targets.csv', header=None).T\n",
"labels.columns = ['Benign', 'Malignant']"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"id": "equal-cooling",
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"metadata": {},
"outputs": [],
"source": [
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"data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"id": "valuable-illinois",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Model: \"sequential_1\"\n",
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"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
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"dense_2 (Dense) (None, 50) 500 \n",
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"_________________________________________________________________\n",
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"dense_3 (Dense) (None, 2) 102 \n",
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"=================================================================\n",
"Total params: 602\n",
"Trainable params: 602\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"layers = [tf.keras.layers.InputLayer(input_shape=(9,)), \n",
" tf.keras.layers.Dense(50, activation='sigmoid'), \n",
" tf.keras.layers.Dense(2, activation='softmax')]\n",
"\n",
"model = tf.keras.models.Sequential(layers)\n",
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"model.compile('sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
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"model.summary()"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "opened-terminology",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Epoch 1/50\n",
"10/10 [==============================] - 0s 16ms/step - loss: 0.6577 - accuracy: 0.6181 - val_loss: 0.6124 - val_accuracy: 0.6857\n"
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]
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}
],
"source": [
"e=50\n",
"history = model.fit(data_train, labels_train, epochs=e, validation_split=0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "forward-asthma",
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"metadata": {},
"outputs": [],
"source": [
"model.evaluate(data_test, \n",
" labels_test, \n",
" batch_size=128)"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "local-program",
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"metadata": {},
"outputs": [],
"source": [
"json.loads(model.to_json())"
]
},
{
"cell_type": "code",
"execution_count": 8,
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"id": "animated-raise",
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"metadata": {},
"outputs": [
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{
"data": {
"text/plain": [
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"{'_self_setattr_tracking': True,\n",
" '_is_model_for_instrumentation': True,\n",
" '_instrumented_keras_api': True,\n",
" '_instrumented_keras_layer_class': False,\n",
" '_instrumented_keras_model_class': True,\n",
" '_trainable': True,\n",
" '_stateful': False,\n",
" 'built': True,\n",
" '_build_input_shape': TensorShape([None, 9]),\n",
" '_saved_model_inputs_spec': TensorSpec(shape=(None, 9), dtype=tf.float32, name='input_1'),\n",
" '_input_spec': None,\n",
" '_supports_masking': True,\n",
" '_name': 'sequential',\n",
" '_activity_regularizer': None,\n",
" '_trainable_weights': [],\n",
" '_non_trainable_weights': [],\n",
" '_updates': [],\n",
" '_thread_local': <_thread._local at 0x1e9b3938c70>,\n",
" '_callable_losses': [],\n",
" '_losses': [],\n",
" '_metrics': [],\n",
" '_metrics_lock': <unlocked _thread.lock object at 0x000001E9CF471E40>,\n",
" '_dtype_policy': <Policy \"float32\">,\n",
" '_compute_dtype_object': tf.float32,\n",
" '_autocast': False,\n",
" '_layers': [<tensorflow.python.keras.engine.input_layer.InputLayer at 0x1e9cf471eb0>,\n",
" <tensorflow.python.keras.layers.core.Dense at 0x1e9cf4c8f10>,\n",
" <tensorflow.python.keras.layers.core.Dense at 0x1e9cf518250>],\n",
" '_inbound_nodes_value': [],\n",
" '_outbound_nodes_value': [],\n",
" '_expects_training_arg': True,\n",
" '_default_training_arg': None,\n",
" '_expects_mask_arg': True,\n",
" '_dynamic': False,\n",
" '_initial_weights': None,\n",
" '_auto_track_sub_layers': False,\n",
" '_preserve_input_structure_in_config': False,\n",
" '_is_graph_network': True,\n",
" 'inputs': [<KerasTensor: shape=(None, 9) dtype=float32 (created by layer 'input_1')>],\n",
" 'outputs': [<KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'dense_1')>],\n",
" 'input_names': ['input_1'],\n",
" 'output_names': ['dense_1'],\n",
" '_compute_output_and_mask_jointly': True,\n",
" '_distribution_strategy': None,\n",
" 'predict_function': None,\n",
" '_compiled_trainable_state': <WeakKeyDictionary at 0x1e9cf8449a0>,\n",
" '_training_state': None,\n",
" '_trackable_saver': <tensorflow.python.training.tracking.util.TrackableSaver at 0x1e9cf518640>,\n",
" '_steps_per_execution': <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=1>,\n",
" '_train_counter': <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=500>,\n",
" '_test_counter': <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=3>,\n",
" '_predict_counter': <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=0>,\n",
" '_base_model_initialized': True,\n",
" '_inferred_input_shape': None,\n",
" '_has_explicit_input_shape': True,\n",
" '_input_dtype': None,\n",
" '_layer_call_argspecs': {<tensorflow.python.keras.engine.input_layer.InputLayer at 0x1e9cf471eb0>: FullArgSpec(args=['self', 'inputs'], varargs=None, varkw='kwargs', defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={}),\n",
" <tensorflow.python.keras.layers.core.Dense at 0x1e9cf4c8f10>: FullArgSpec(args=['self', 'inputs'], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={}),\n",
" <tensorflow.python.keras.layers.core.Dense at 0x1e9cf518250>: FullArgSpec(args=['self', 'inputs'], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={})},\n",
" '_created_nodes': set(),\n",
" '_graph_initialized': True,\n",
" '_use_legacy_deferred_behavior': False,\n",
" '_nested_inputs': <KerasTensor: shape=(None, 9) dtype=float32 (created by layer 'input_1')>,\n",
" '_nested_outputs': <KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'dense_1')>,\n",
" '_enable_dict_to_input_mapping': True,\n",
" '_input_layers': [<tensorflow.python.keras.engine.input_layer.InputLayer at 0x1e9cf471eb0>],\n",
" '_output_layers': [<tensorflow.python.keras.layers.core.Dense at 0x1e9cf518250>],\n",
" '_input_coordinates': [(<tensorflow.python.keras.engine.input_layer.InputLayer at 0x1e9cf471eb0>,\n",
" 0,\n",
" 0)],\n",
" '_output_coordinates': [(<tensorflow.python.keras.layers.core.Dense at 0x1e9cf518250>,\n",
" 0,\n",
" 0)],\n",
" '_output_mask_cache': {},\n",
" '_output_tensor_cache': {},\n",
" '_output_shape_cache': {},\n",
" '_network_nodes': {'dense_1_ib-0', 'dense_ib-0', 'input_1_ib-0'},\n",
" '_nodes_by_depth': defaultdict(list,\n",
" {0: [<tensorflow.python.keras.engine.node.Node at 0x1e9cf831f70>],\n",
" 1: [<tensorflow.python.keras.engine.node.Node at 0x1e9cf518190>],\n",
" 2: [<tensorflow.python.keras.engine.node.Node at 0x1e9cf4c8d30>]}),\n",
" '_feed_input_names': ['input_1'],\n",
" '_feed_inputs': [<KerasTensor: shape=(None, 9) dtype=float32 (created by layer 'input_1')>],\n",
" '_feed_input_shapes': [(None, 9)],\n",
" '_tensor_usage_count': Counter({'2103716908480': 1,\n",
" '2103720484432': 1,\n",
" '2103720530848': 1}),\n",
" '_obj_reference_counts_dict': ObjectIdentityDictionary({<_ObjectIdentityWrapper wrapping <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD object at 0x000001E9CF518430>>: 1, <_ObjectIdentityWrapper wrapping <tensorflow.python.keras.engine.compile_utils.LossesContainer object at 0x000001E9CF83D8E0>>: 1, <_ObjectIdentityWrapper wrapping <tensorflow.python.keras.engine.compile_utils.MetricsContainer object at 0x000001E9CF844940>>: 1, <_ObjectIdentityWrapper wrapping True>: 1, <_ObjectIdentityWrapper wrapping 'categorical_crossentropy'>: 1, <_ObjectIdentityWrapper wrapping <tensorflow.python.eager.def_function.Function object at 0x000001E9D6A4AEE0>>: 1, <_ObjectIdentityWrapper wrapping <tensorflow.python.eager.def_function.Function object at 0x000001E9F00CBA90>>: 1, <_ObjectIdentityWrapper wrapping <tensorflow.python.keras.callbacks.History object at 0x000001E9F2BBFB50>>: 1}),\n",
" '_run_eagerly': None,\n",
" '_self_unconditional_checkpoint_dependencies': [TrackableReference(name='optimizer', ref=<tensorflow.python.keras.optimizer_v2.gradient_descent.SGD object at 0x000001E9CF518430>)],\n",
" '_self_unconditional_dependency_names': {'optimizer': <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD at 0x1e9cf518430>},\n",
" '_self_unconditional_deferred_dependencies': {},\n",
" '_self_update_uid': -1,\n",
" '_self_name_based_restores': set(),\n",
" '_self_saveable_object_factories': {},\n",
" 'optimizer': <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD at 0x1e9cf518430>,\n",
" 'compiled_loss': <tensorflow.python.keras.engine.compile_utils.LossesContainer at 0x1e9cf83d8e0>,\n",
" 'compiled_metrics': <tensorflow.python.keras.engine.compile_utils.MetricsContainer at 0x1e9cf844940>,\n",
" '_is_compiled': True,\n",
" 'loss': 'categorical_crossentropy',\n",
" 'stop_training': False,\n",
" 'train_function': <tensorflow.python.eager.def_function.Function at 0x1e9d6a4aee0>,\n",
" 'history': <tensorflow.python.keras.callbacks.History at 0x1e9f2bbfb50>,\n",
" 'test_function': <tensorflow.python.eager.def_function.Function at 0x1e9f00cba90>}"
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]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"model.__dict__"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "third-accuracy",
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"metadata": {},
"outputs": [],
"source": [
"model.optimizer.get_config()"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"id": "synthetic-armstrong",
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"metadata": {},
"outputs": [
{
"data": {
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exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"text/plain": [
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"<Figure size 3000x1400 with 2 Axes>"
exp2 draft, exporting results
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]
},
pickling results, plotting from dataset only
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"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
exp2 draft, exporting results
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}
],
"source": [
pickling results, plotting from dataset only
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"fig, axes = plt.subplots(1, 2, figsize=(15,7))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"ax = axes[0]\n",
"ax.set_title(\"Training vs Validation Loss\")\n",
"ax.plot(history.history['loss'], label=\"train\", lw=2)\n",
"ax.plot(history.history['val_loss'], label=\"validation\", lw=2, c=(1,0,0))\n",
"ax.set_xlabel(\"Epochs\")\n",
"ax.legend()\n",
"\n",
"ax = axes[1]\n",
"ax.set_title(\"Training vs Validation Accuracy\")\n",
"ax.plot(history.history['accuracy'], label=\"train\", lw=2)\n",
"ax.plot(history.history['val_accuracy'], label=\"validation\", lw=2, c=(1,0,0))\n",
"ax.set_xlabel(\"Epochs\")\n",
"ax.set_ylim(0, 1)\n",
"ax.legend()\n",
"\n",
"plt.show()"
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
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"execution_count": 7,
exp2 agreement, individual accuracy
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"id": "coordinated-salvation",
exp2 draft, exporting results
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"metadata": {},
"outputs": [],
"source": [
"tensor = model(data_test.to_numpy())"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
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"execution_count": 8,
exp2 agreement, individual accuracy
2021-03-26 20:01:05 +00:00
"id": "roman-explorer",
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(350, 2), dtype=float32, numpy=\n",
pickling results, plotting from dataset only
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exp2 draft, exporting results
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]
},
pickling results, plotting from dataset only
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"execution_count": 8,
exp2 draft, exporting results
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tensor"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 9,
exp2 agreement, individual accuracy
2021-03-26 20:01:05 +00:00
"id": "caring-assets",
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"outputs": [
{
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pickling results, plotting from dataset only
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pickling results, plotting from dataset only
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exp2 draft, exporting results
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pickling results, plotting from dataset only
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exp2 draft, exporting results
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pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
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exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
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pickling results, plotting from dataset only
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exp2 draft, exporting results
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" [0., 1.]], dtype=float32)>"
]
},
pickling results, plotting from dataset only
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"execution_count": 9,
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.math.round(tensor)"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 10,
exp2 agreement, individual accuracy
2021-03-26 20:01:05 +00:00
"id": "controversial-modern",
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 0])>"
]
},
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 10,
exp2 draft, exporting results
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.constant([1, 0]) + tf.constant([1, 0])"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 11,
exp2 agreement, individual accuracy
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"id": "correct-lodging",
exp2 draft, exporting results
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])>"
]
},
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 11,
exp2 draft, exporting results
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.constant([1, 0]) == tf.constant([0, 1])"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 12,
exp2 agreement, individual accuracy
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"id": "suited-standard",
exp2 draft, exporting results
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor([1 0], shape=(2,), dtype=int64)\n",
"tf.Tensor([ True True], shape=(2,), dtype=bool)\n",
"tf.Tensor(0, shape=(), dtype=int64)\n",
"\n",
"tf.Tensor([1 0], shape=(2,), dtype=int64)\n",
"tf.Tensor([ True True], shape=(2,), dtype=bool)\n",
"tf.Tensor(0, shape=(), dtype=int64)\n",
"\n",
"tf.Tensor([1 0], shape=(2,), dtype=int64)\n",
"tf.Tensor([ True True], shape=(2,), dtype=bool)\n",
"tf.Tensor(0, shape=(), dtype=int64)\n",
"\n",
"tf.Tensor([0 1], shape=(2,), dtype=int64)\n",
"tf.Tensor([False False], shape=(2,), dtype=bool)\n",
"tf.Tensor(1, shape=(), dtype=int64)\n",
"\n",
"tf.Tensor([1 0], shape=(2,), dtype=int64)\n",
"tf.Tensor([ True True], shape=(2,), dtype=bool)\n",
"tf.Tensor(0, shape=(), dtype=int64)\n",
"\n"
]
}
],
"source": [
"for c in tf.constant(labels_test)[:5]:\n",
" print(c)\n",
" print(c == tf.constant([1, 0], dtype='int64'))\n",
" print(tf.math.argmax(c))\n",
" print()"
]
},
{
"cell_type": "code",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 13,
exp2 agreement, individual accuracy
2021-03-26 20:01:05 +00:00
"id": "greater-publisher",
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 2])>"
]
},
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"execution_count": 13,
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.math.reduce_sum(tf.constant([[0, 1], [0, 1]]), axis=0)"
]
},
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
{
"cell_type": "code",
"execution_count": 9,
exp2 agreement, individual accuracy
2021-03-26 20:01:05 +00:00
"id": "considerable-fluid",
pickling results, plotting from dataset only
2021-03-21 09:56:27 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5.])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(5, 150, num=1)"
]
exp2 draft, exporting results
2021-03-19 17:21:00 +00:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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