markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Mientras que la siguiente dará un error: | M + b[:2] | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
El error es claro, *NumPy* no sabe cómo hacer para encajar las dimensiones de estos dos arreglos. Más detalles sobre broadcasting [aquí](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). Comparaciones y máscaras de booleanos. Así como es posible sumar un número a un arreglo, también es posible hacer com... | M > 3 | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Es muy común usar el resultado de tal comparación para obtener valores de un arreglo que cumplan con cierto criterio, como: | M[M > 3] | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
O incluso combinando arreglos, como: | M[a == 2] | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Medidas de centralidad y dispersión. *NumPy* nos permite calcular la media, la mediana y la varianza a partir de arrays de forma muy simple. Por ejemplo para calcular la media podemos usar la función np.mean(): | np.mean(v) | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Una forma alternativa es usar el método *`.mean()`* de un objeto *array*: | v.mean() | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Las funciones y métodos *`var`* y *`std`* calculan la *varianza* y la *desviación* de un *array*: | np.var(v) # Varianza de los elementos de v.
v.var() # Forma alternativa.
np.std(v) # Desviación estándar de v.
v.std() # Forma alternativa. | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Existen otras medidas para caracterizar los datos, llamadas de forma, como son la [*curtosis*](https://es.wikipedia.org/wiki/Curtosis) y el [*sesgo*](https://es.wikipedia.org/wiki/Sesgo_estadístico) (o asimetría estadística).Estás medidas son menos usadas en parte porque su interpretación es menos intuitiva que otras m... | x = np.random.rand(100)
np.percentile(x , [25, 50, 75]) | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
Z-score. El *Z-score* es una cantidad adimensional que expresa el número de desviaciones estándar que un dato está por encima o por debajo de la media. Si el *Z-score* es positivo el dato está por encima de la media, y cuando es negativo está por debajo de la media. Se calcula como:$$z = \frac{x - \mu}{\sigma}$$Donde:... | np.dot(a, b) # Producto escalar de los vectores a y b.
a@b # Forma alternativa del producto escalar de los vectores a y b con el operador @.
I = np.identity(3) # Retorna la matriz identidad de 3x3.
I
np.dot(M, I) # Multiplicación de las matrices M e I.
M@I # Forma alternativa de la multiplicación de las matrices M e I ... | _____no_output_____ | MIT | 01_numpy.ipynb | ejdecena/herramientas |
BASIC SETUP | # BASIC SETUP
! [ ! -z "$COLAB_GPU" ] && pip install torch skorch && pip install neptune-client
!cp "drive/My Drive/dl_project_data/repo/data_loading.py" .
!mkdir ./helper_scripts/
!cp "drive/My Drive/dl_project_data/repo/helper_scripts/visual_helpers.py" ./helper_scripts
!cp "drive/My Drive/dl_project_data/repo/archi... | Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.5.1+cu101)
Collecting skorch
[?25l Downloading https://files.pythonhosted.org/packages/42/21/4936b881b33de285faa0b36209afe4f9724a0875b2225abdc63b23d384a3/skorch-0.8.0-py3-none-any.whl (113kB)
[K |████████████████████████████████| 1... | MIT | notebooks/VGG19_training.ipynb | tranic/histopathology_cancer_detection |
IMPORTS**You should not have to change anything here.** | # IMPORTS
from collections import OrderedDict
import torch
from torch import nn
from torchvision import models, transforms
import skorch.callbacks as scb
from skorch import NeuralNetBinaryClassifier
import model_training as md
import architecture as arch
from data_loading import ToTensor, Normalize, RandomRotation, ... | _____no_output_____ | MIT | notebooks/VGG19_training.ipynb | tranic/histopathology_cancer_detection |
CLASSIFIER PARAMETRIZATIONHere you can parametrize your model and set loss, optimizer, learning rate, etc. For further information on what can be set and how, please refer to the [skorch documentation](https://skorch.readthedocs.io/en/stable/classifier.htmlskorch.classifier.NeuralNetClassifier). | # CLASSIFIER PARAMETRIZATION
classifier = NeuralNetBinaryClassifier(
arch.VGG19,
optimizer = torch.optim.Adamax,
max_epochs = 30,
lr = 0.002,
batch_size = 128,
iterator_train__shuffle = True, # Shuffle training data on each epoch
train_split = None,
callbacks = [scb.LRScheduler(policy =... | _____no_output_____ | MIT | notebooks/VGG19_training.ipynb | tranic/histopathology_cancer_detection |
CLASSIFIER TRAININGAfter you have added the shared folder with the data to your drive as a shortcut, you should not have to change anything here. At least for now.**IF YOU WANT TO TRAIN WITH THE FULL DATASET, JUST REMOVE** *_small* **FROM THE CSV FILE.** | # CLASSIFIER TRAINING
md.train_model(classifier,
train_labels = "drive/My Drive/dl_project_data/train/train_split.csv",
test_labels = "drive/My Drive/dl_project_data/train/test_split.csv",
file_dir = "train",
train_transform = transforms.Compose([transforms.ToPILImage... | https://ui.neptune.ai/elangenhan/hcd-experiments/e/HCDEX-3
Starting Training for <class 'architecture.VGG19'>
[1mModel-Params:[0m
[1mCriterion:[0m <class 'torch.nn.modules.loss.BCEWithLogitsLoss'>
[1mOptimizer:[0m <class 'torch.optim.adamax.Adamax'>
[1... | MIT | notebooks/VGG19_training.ipynb | tranic/histopathology_cancer_detection |
Image recognition: recognizing hurricane damage Daniel Buscombe, MARDA ScienceMIT LicenseCopyright (c) 2020, Marda Science LLCPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associat... | !pip install tf-nightly --quiet
import requests, os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import models
tf.__version__
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
# from https://stackoverflow.com/questions/38511444/py... | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
What class categories do I have? | !ls train | damage no_damage
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
How many train files? | !ls train/damage | wc -l
!ls train/no_damage | wc -l | 5000
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
How many test and validation files? | !ls test/damage | wc -l
!ls test/no_damage | wc -l
!ls validation/damage | wc -l
!ls validation/no_damage | wc -l | 1000
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Define text labels for our two classes | classes = ['damage', 'no_damage'] | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Get rid of any corrupt jpegs | num_skipped = 0
for folder in ['test', 'train', 'validation']:
for folder_name in classes:
folder_path = os.path.join(folder, folder_name)
for fname in os.listdir(folder_path):
fpath = os.path.join(folder_path, fname)
fobj = open(fpath, 'rb')
if tf.compat.as_bytes('JFIF') not in fobj.peek(10... | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Augmenting the data | image_size = (128, 128)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
'train', seed=2020,
image_size=image_size, batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
'validation', seed=2020,
image_size=image_size, batch_size=batch_size... | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Improve model throughput by using [pre-fetch](https://www.tensorflow.org/api_docs/python/tf/data/Datasetprefetch) | train_ds = train_ds.prefetch(buffer_size=batch_size)
val_ds = val_ds.prefetch(buffer_size=batch_size)
augmented_train_ds = train_ds.map(
lambda x, y: (data_augmentation(x, training=True), y)) | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Image classification using transfer learning Build a model Load the MobileNetV2 model trained on imagenet, but exclude the classification layers, because we want to add our own classification layers so we can retrain the model on our own categories We'll use one of the 'stock' models provided by `keras.applications` c... | def mobilenet_model(num_classes, input_shape):
EXTRACTOR = MobileNetV2(include_top=False, weights="imagenet",
input_shape=input_shape)
EXTRACTOR.trainable = True
# Construct the head of the model that will be placed on top of the
# the base model
class_head = EXTRACTOR.output
... | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Train the model | min_lr = 1e-4
patience = 5
factor = 0.8
cooldown = 3
epochs = 50
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
filepath = 'hurricanes_mn2_best_weights.h5'
earlystop = EarlyStopping(monitor="val_loss",
mode="min", patience=patience)
# reduction... | WARNING:tensorflow:From <ipython-input-40-4cca302ae1c0>:4: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Epoch 1/50
WARNING:tensorflow:Model was constructed with shape ... | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Run inference on new dataDropout are inactive at inference time, so that layer won't affect our model results | f = glob('test/no_damage/*.jpeg')[0]
img = keras.preprocessing.image.load_img(f, target_size=image_size)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create batch axis
scores = model2.predict(img_array).flatten()
print(classes[np.argmax(scores)]) | no_damage
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
We can use the `model.evaluate()` function to evaluate the average accuracy for the entire test set | scores = model2.evaluate(val_ds) | 2000/2000 [==============================] - 11s 5ms/step - loss: 0.0223 - accuracy: 0.9920
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Plotting the confusion matrix The confusion matrix is one of correspondences between actual and predicted labels, per class | from sklearn.metrics import confusion_matrix
import seaborn as sns | /usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Get a new validation batch generator with a batch size of 1, and shuffling set to False because we want to pair each image with its class | val_ds = tf.keras.preprocessing.image_dataset_from_directory(
'test', seed=2020, shuffle=False,
image_size=image_size, batch_size=1) | Found 2000 files belonging to 2 classes.
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Get the image class labels and store in a list `L` | L = []
for _, labels in val_ds:
L.append(int(labels[0]))
np.bincount(L) | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Use the trained model to make predictions on the test set | preds = model2.predict(val_ds)
pred = np.argmax(preds, axis=1) | WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input Tensor("input_1:0", shape=(None, 224, 224, 3), dtype=float32), but it was called on an input with incompatible shape (None, 128, 128, None).
| MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Get the confusion matrix (the matrix of label correspondences between ground truth and model prediction) | cm = confusion_matrix(np.asarray(L), pred)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Make a plot of that matrix | plt.figure(figsize=(15,15))
sns.heatmap(cm,
annot=True,
cmap = sns.cubehelix_palette(dark=0, light=1, as_cmap=True))
tick_marks = np.arange(len(classes))+.5
plt.xticks(tick_marks, classes, rotation=45,fontsize=10)
plt.yticks(tick_marks, classes,rotation=45, fontsize=10) | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage |
Both classes are estimated to within 1% | _____no_output_____ | MIT | MARDA_Image_recognition_3_hurricanes.ipynb | dbuscombe-usgs/HurricaneHarvey_buildingdamage | |
DIY Notebook Goals Understand the role of number of epochs in transfer learning Till what point increasing epochs helps in imporving acuracy How overtraining can result in overfitting the data You will be using skin-cancer mnist to train the classifiers Table of Contents [0. Install](0) [1. Train a resnet50 network... | !git clone https://github.com/Tessellate-Imaging/monk_v1.git
# If using Colab install using the commands below
!cd monk_v1/installation/Misc && pip install -r requirements_colab.txt
# If using Kaggle uncomment the following command
#!cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt
# Select the ... | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Dataset Details - Credits: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000 - Seven classes - benign_keratosis_like_lesions - melanocytic_nevi - dermatofibroma - melanoma - vascular_lesions - basal_cell_carcinoma - Bowens_disease Download the dataset | ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1MRC58-oCdR1agFTWreDFqevjEOIWDnYZ' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1MRC5... | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Imports | # Monk
import os
import sys
sys.path.append("monk_v1/monk/");
#Using keras backend
from keras_prototype import prototype | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Train a resnet50 network for 5 epochs Creating and managing experiments - Provide project name - Provide experiment name - For a specific data create a single project - Inside each project multiple experiments can be created - Every experiment can be have diferent hyper-parameters attached to it | gtf = prototype(verbose=1);
gtf.Prototype("Project", "Epochs-5"); | Keras Version: 2.2.5
Tensorflow Version: 1.12.0
Experiment Details
Project: Project
Experiment: Epochs-5
Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/3_training_epochs/workspace/Project/E... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
This creates files and directories as per the following structure workspace | |--------Project | | |-----Freeze_Base_Network | |-----experiment-state.json ... | gtf.Default(dataset_path="skin_cancer_mnist_dataset/images",
path_to_csv="skin_cancer_mnist_dataset/train_labels.csv",
model_name="resnet50",
freeze_base_network=True,
num_epochs=5); #Set number of epochs here
#Read the summary... | Dataset Details
Train path: skin_cancer_mnist_dataset/images
Val path: None
CSV train path: skin_cancer_mnist_dataset/train_labels.csv
CSV val path: None
Dataset Params
Input Size: 224
Batch Size: 4
Data Shuffle: True
Processors: 4
Train-val split: 0.7
Delimiter... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
From summary above Training params Num Epochs: 5 Train the classifier | #Start Training
gtf.Train();
#Read the training summary generated once you run the cell and training is completed | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Final training loss - Final validation loss - (You may get a different result) Re-Train a new experiment for 10 epochs Creating and managing experiments - Provide project name - Provide experiment name - For a specific data create a single project - Inside each project multiple experiments can be creat... | gtf = prototype(verbose=1);
gtf.Prototype("Project", "Epochs-10"); | Keras Version: 2.2.5
Tensorflow Version: 1.12.0
Experiment Details
Project: Project
Experiment: Epochs-10
Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/3_training_epochs/workspace/Project/... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
This creates files and directories as per the following structure workspace | |--------Project | | |-----Epochs-5 (Previously created) | |-----experiment-state.json ... | gtf.Default(dataset_path="skin_cancer_mnist_dataset/images",
path_to_csv="skin_cancer_mnist_dataset/train_labels.csv",
model_name="resnet50",
freeze_base_network=True,
num_epochs=10); #Set number of epochs here
#Read the summar... | Dataset Details
Train path: skin_cancer_mnist_dataset/images
Val path: None
CSV train path: skin_cancer_mnist_dataset/train_labels.csv
CSV val path: None
Dataset Params
Input Size: 224
Batch Size: 4
Data Shuffle: True
Processors: 4
Train-val split: 0.7
Delimiter... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
From summary above Training params Num Epochs: 10 Train the classifier | #Start Training
gtf.Train();
#Read the training summary generated once you run the cell and training is completed | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Final training loss - Final validation loss - (You may get a different result) | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 | |
Re-Train a third experiment for 20 epochs Creating and managing experiments - Provide project name - Provide experiment name - For a specific data create a single project - Inside each project multiple experiments can be created - Every experiment can be have diferent hyper-parameters attached to it | gtf = prototype(verbose=1);
gtf.Prototype("Project", "Epochs-20"); | Keras Version: 2.2.5
Tensorflow Version: 1.12.0
Experiment Details
Project: Project
Experiment: Epochs-20
Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/3_training_epochs/workspace/Project/... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
This creates files and directories as per the following structure workspace | |--------Project | | |-----Epochs-5 (Previously created) | |-----experiment-state.json ... | gtf.Default(dataset_path="skin_cancer_mnist_dataset/images",
path_to_csv="skin_cancer_mnist_dataset/train_labels.csv",
model_name="resnet50",
freeze_base_network=True,
num_epochs=20); #Set number of epochs here
#Read the summar... | Dataset Details
Train path: skin_cancer_mnist_dataset/images
Val path: None
CSV train path: skin_cancer_mnist_dataset/train_labels.csv
CSV val path: None
Dataset Params
Input Size: 224
Batch Size: 4
Data Shuffle: True
Processors: 4
Train-val split: 0.7
Delimiter... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
From summary above Training params Num Epochs: 20 Train the classifier | #Start Training
gtf.Train();
#Read the training summary generated once you run the cell and training is completed | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Final training loss - Final validation loss - (You may get a different result) Compare the experiments | # Invoke the comparison class
from compare_prototype import compare | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Creating and managing comparison experiments - Provide project name | # Create a project
gtf = compare(verbose=1);
gtf.Comparison("Compare-effect-of-num-epochs"); | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
This creates files and directories as per the following structure workspace | |--------comparison | | |-----Compare-effect-of-num-epochs | |------stats_best_va... | gtf.Add_Experiment("Project", "Epochs-5");
gtf.Add_Experiment("Project", "Epochs-10");
gtf.Add_Experiment("Project", "Epochs-20"); | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Run Analysis | gtf.Generate_Statistics(); | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Visualize and study comparison metrics Training Accuracy Curves | from IPython.display import Image
Image(filename="workspace/comparison/Compare-effect-of-num-epochs/train_accuracy.png") | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Training Loss Curves | from IPython.display import Image
Image(filename="workspace/comparison/Compare-effect-of-num-epochs/train_loss.png") | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Validation Accuracy Curves | from IPython.display import Image
Image(filename="workspace/comparison/Compare-effect-of-num-epochs/val_accuracy.png") | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Validation loss curves | from IPython.display import Image
Image(filename="workspace/comparison/Compare-effect-of-num-epochs/val_loss.png") | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/4_effect_of_training_epochs/3) Understand the effect of number of epochs in transfer learning - keras.ipynb | shubham7169/monk_v1 |
Examine a sample imageMuch of what is describe here has been borrowed from the following resourceshttps://github.com/blaylockbk/pyBKB_v2/blob/master/BB_goes16/mapping_GOES16_data.ipynbhttp://edc.occ-data.org/goes16/python/http://www.ceda.ac.uk/static/media/uploads/ncas-reading-2015/10_read_netcdf_python.pdf Import the... | %matplotlib inline
from netCDF4 import Dataset
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
from pyproj import Proj
import datetime
from mpl_toolkits.basemap import Basemap
from osgeo import gdal
os.chdir("/Users/nathan/Documents/Projects/GOES_Fire_Growth/Raw_Data") | _____no_output_____ | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Import dataset and examine dimensions | C_file = Dataset("OR_ABI-L2-CMIPC-M3C07_G16_s20172830932227_e20172830935012_c20172830935048.nc", 'r')
ref_ch7 = C_file.variables['CMI'][:]
#C_file.close()
#C_file = None
print C_file.file_format
dims = C_file.dimensions.keys()
print dims
for dim in dims:
print C_file.dimensions[dim] | NETCDF4
[u'y', u'x', u'number_of_time_bounds', u'band', u'number_of_image_bounds']
<type 'netCDF4._netCDF4.Dimension'>: name = 'y', size = 1500
<type 'netCDF4._netCDF4.Dimension'>: name = 'x', size = 2500
<type 'netCDF4._netCDF4.Dimension'>: name = 'number_of_time_bounds', size = 2
<type 'netCDF4._netCDF4.Dimension'... | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Examine Variables | print C_file.variables.keys()
print C_file.variables["goes_imager_projection"] | [u'CMI', u'DQF', u't', u'y', u'x', u'time_bounds', u'goes_imager_projection', u'y_image', u'y_image_bounds', u'x_image', u'x_image_bounds', u'nominal_satellite_subpoint_lat', u'nominal_satellite_subpoint_lon', u'nominal_satellite_height', u'geospatial_lat_lon_extent', u'band_wavelength', u'band_id', u'total_number_of_p... | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Get time | # Data are stored as seconds since 2000-01-01 12:00:00
secs = C_file.variables['t'][0]
img_date = datetime.datetime(2000, 1, 1, 12) + datetime.timedelta(seconds = secs) | _____no_output_____ | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Get image data | b = C_file.variables['CMI']
# Plot it
plt.figure(figsize = [8,8])
plt.imshow(b)
plt.title(img_date)
bt = np.array(b) > 295
# Plot it
plt.figure(figsize = [8,8])
plt.imshow(bt)
plt.title(img_date) | _____no_output_____ | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Get projection and location info | sh = C_file.variables['goes_imager_projection'].perspective_point_height
slon = C_file.variables['goes_imager_projection'].longitude_of_projection_origin
ssweep = C_file.variables['goes_imager_projection'].sweep_angle_axis
# Get coordinates
xcoords = C_file.variables['x'][:] * sh
ycoords = C_file.variables['y'][:] * s... | _____no_output_____ | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Subset to North Bay area | nbb = b[325:475, 30:120]
# Plot it
plt.figure(figsize = [8,8])
plt.imshow(nbb)
plt.title(img_date)
# Histogram
plt.hist(np.concatenate(nbb))
nbbt = nbb > 295
# Plot it
plt.figure(figsize = [8,8])
plt.imshow(nbbt)
plt.title(img_date) | _____no_output_____ | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Try writing raster | driver = gdal.GetDriverByName('GTiff')
new_file = driver.Create('test_band1.tif',
C_file.dimensions['x'].size, # number of columns
C_file.dimensions['y'].size, # number of rows
1, # number of bands
gdal.GDT_Float32) # datat... | Help on getset descriptor _proj.Proj.srs:
srs
['__call__', '__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_fwd',... | MIT | .ipynb_checkpoints/Raster_manipulation-checkpoint.ipynb | pavlovc2/goes_r_fire |
Statistics from Stock DataIn this lab we will load stock data into a Pandas Dataframe and calculate some statistics on it. We will be working with stock data from Google, Apple, and Amazon. All the stock data was downloaded from yahoo finance in CSV format. In your workspace you should have a file named GOOG.csv conta... | # We import pandas into Python
import pandas as pd
# We read in a stock data data file into a data frame and see what it looks like
df = pd.read_csv('./GOOG.csv')
# We display the first 5 rows of the DataFrame
df.head() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
We clearly see that the Dataframe is has automatically labeled the row indices using integers and has labeled the columns of the DataFrame using the names of the columns in the CSV files. To DoYou will now load the stock data from Google, Apple, and Amazon into separte DataFrames. However, for each stock data you will ... | # We load the Google stock data into a DataFrame
google_stock = pd.read_csv('./GOOG.csv', parse_dates=True)
# We load the Apple stock data into a DataFrame
apple_stock = pd.read_csv('./AAPL.csv', parse_dates=True)
# We load the Amazon stock data into a DataFrame
amazon_stock = pd.read_csv('./AMZN.csv', parse_dates=Tr... | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
You can check that you have loaded the data correctly by displaying the head of the DataFrames. | # We display the google_stock DataFrame
google_stock.head() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
You will now join the three DataFrames above to create a single new DataFrame that contains all the `Adj Close` for all the stocks. Let's start by creating an empty DataFrame that has as row indices calendar days between `2000-01-01` and `2016-12-31`. We will use the `pd.date_range()` function to create the calendar d... | # We create calendar dates between '2000-01-01' and '2016-12-31'
dates = pd.date_range('2000-01-01', '2016-12-31')
# We create and empty DataFrame that uses the above dates as indices
all_stocks = pd.DataFrame(index = dates) | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
To DoYou will now join the the individual DataFrames, `google_stock`, `apple_stock`, and `amazon_stock`, to the `all_stocks` DataFrame. However, before you do this, it is necessary that you change the name of the columns in each of the three dataframes. This is because the column labels in the `all_stocks` dataframe m... | # Change the Adj Close column label to Google
google_stock = google_stock.rename(columns = {'Adj Close': 'Google'})
# Change the Adj Close column label to Apple
apple_stock = apple_stock.rename(columns = {'Adj Close': 'Apple'})
# Change the Adj Close column label to Amazon
amazon_stock = amazon_stock.rename(columns =... | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
You can check that the column labels have been changed correctly by displaying the datadrames | # We display the google_stock DataFrame
google_stock.head()
# We display the apple_stock DataFrame
apple_stock.head()
# We display the amazon_stock DataFrame
amazon_stock.head() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
Now that we have unique column labels, we can join the individual DataFrames to the `all_stocks` DataFrame. For this we will use the `dataframe.join()` function. The function `dataframe1.join(dataframe2)` joins `dataframe1` with `dataframe2`. We will join each dataframe one by one to the `all_stocks` dataframe. Fill in... | # We join the Google stock to all_stocks
all_stocks = all_stocks.join(google_stock, lsuffix="_all_stocks", rsuffix="_google")
# We join the Apple stock to all_stocks
all_stocks = all_stocks.join(apple_stock, lsuffix="_all_stocks", rsuffix="_google")
# We join the Amazon stock to all_stocks
all_stocks =all_stocks.join... | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
You can check that the dataframes have been joined correctly by displaying the `all_stocks` dataframe | # We display the all_stocks DataFrame
all_stocks.head() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
To DoBefore we proceed to get some statistics on the stock data, let's first check that we don't have any *NaN* values. In the space below check if there are any *NaN* values in the `all_stocks` dataframe. If there are any, remove any rows that have *NaN* values: | # Check if there are any NaN values in the all_stocks dataframe
all_stocks.isnull().sum().sum()
# Remove any rows that contain NaN values
all_stocks.dropna(axis = 0) | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
You can check that the *NaN* values have been eliminated by displaying the `all_stocks` dataframe | # Check if there are any NaN values in the all_stocks dataframe
| _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
Display the `all_stocks` dataframe and verify that there are no *NaN* values | # We display the all_stocks DataFrame
all_stocks.head() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
Now that you have eliminated any *NaN* values we can now calculate some basic statistics on the stock prices. Fill in the code below | # Print the average stock price for each stock
all_stocks.fillna(all_stocks.mean(), axis = 0)
# Print the median stock price for each stock
all_stocks.fillna(all_stocks.median(), axis = 0)
# Print the standard deviation of the stock price for each stock
all_stocks.fillna(all_stocks.std(), axis = 0)
# Print the cor... | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
We will now look at how we can compute some rolling statistics, also known as moving statistics. We can calculate for example the rolling mean (moving average) of the Google stock price by using the Pandas `dataframe.rolling().mean()` method. The `dataframe.rolling(N).mean()` calculates the rolling mean over an `N`-day... | # We compute the rolling mean using a 150-Day window for Google stock
rollingMean = dataframe.rolling(150).mean() | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
We can also visualize the rolling mean by plotting the data in our dataframe. In the following lessons you will learn how to use **Matplotlib** to visualize data. For now I will just import matplotlib and plot the Google stock data on top of the rolling mean. You can play around by changing the rolling mean window and ... | # This allows plots to be rendered in the notebook
%matplotlib inline
# We import matplotlib into Python
import matplotlib.pyplot as plt
# We plot the Google stock data
plt.plot(all_stocks['Google'])
# We plot the rolling mean ontop of our Google stock data
plt.plot(rollingMean)
plt.legend(['Google Stock Price', '... | _____no_output_____ | MIT | notes/02 Pandas Mini-Project/Statistics from Stock Data.ipynb | jedrzejpolaczek/AIPND |
异常值处理 | def dropData(train):
# 丢弃部分异常值
train = train[train.area <= 200]
train = train[(train.tradeMoney <=16000) & (train.tradeMoney >=700)]
train.drop(train[(train['totalFloor'] == 0)].index, inplace=True)
# sns.regplot(x=data_train['area'],y=data_train['tradeMoney'])
# plt.show()
return train
#数... | _____no_output_____ | MIT | Trademoney_Prediction.ipynb | aomike/Team_Learning_RentMoney_Prediction |
缺失值处理、数据变换 | def preprocessingData(data):
# 填充缺失值
data['rentType'][data['rentType'] == '--'] = '未知方式'
# 转换object类型数据
columns = ['houseFloor', 'houseToward', 'houseDecoration', 'communityName', 'plate'] # 'rentType', 'houseType',
for feature in columns:
data[feature] = LabelEncoder().fit_transform(d... | _____no_output_____ | MIT | Trademoney_Prediction.ipynb | aomike/Team_Learning_RentMoney_Prediction |
特征选择 | from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()
# 训练随机森林模型，并通过feature_importances_属性获取每个特征的重要性分数。rf = RandomForestRegressor()
train = data_train.drop('tradeMoney', axis=1)
train = train.fillna(0)
y_pred = data_train['tradeMoney']
rf.fit(train, y_pred)
print("Features sorted by their scor... | _____no_output_____ | MIT | Trademoney_Prediction.ipynb | aomike/Team_Learning_RentMoney_Prediction |
- | import warnings
warnings.filterwarnings('ignore')
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidg... | _____no_output_____ | MIT | Trademoney_Prediction.ipynb | aomike/Team_Learning_RentMoney_Prediction |
You can put video within your notebook using `%%html`, writing literal strings or with `IPython.display.HTML`: | %%html
<video width="480" controls
poster="https://archive.org/download/WebmVp8Vorbis/webmvp8.gif" >
<source
src="https://archive.org/download/WebmVp8Vorbis/webmvp8.webm"
type="video/webm">
<source
src="https://archive.org/download/WebmVp8Vorbis/webmvp8_512kb.mp4"
type="video/mp4">
<source
... | _____no_output_____ | BSD-3-Clause | example-notebooks/exploring-elements.ipynb | hussainsultan/vdom |
With vdom, we can create it declaratively | vid = video(source(
src="https://archive.org/download/WebmVp8Vorbis/webmvp8.webm",
type="video/webm"),
source(
src="https://archive.org/download/WebmVp8Vorbis/webmvp8_512kb.mp4",
type="video/mp4"),
source(
src="https://archive.org/download/WebmVp8Vorbis/webmvp8.ogv",
type="video/ogg"),
"Yo... | _____no_output_____ | BSD-3-Clause | example-notebooks/exploring-elements.ipynb | hussainsultan/vdom |
and display it when we want | vid
hand = display(vid, display_id=True) | _____no_output_____ | BSD-3-Clause | example-notebooks/exploring-elements.ipynb | hussainsultan/vdom |
Since you can change attributes of the element directly with `display` updates, we can turn controls off | attrs = vid.attributes.copy()
attrs['controls'] = False
attrs['autoplay'] = False
hand.update(video(vid.children, **attrs)) | _____no_output_____ | BSD-3-Clause | example-notebooks/exploring-elements.ipynb | hussainsultan/vdom |
There are many more elements available | em('what')
strong('bad')
abbr("lol", title="laugh out loud")
time("10/05/13 at 10 PM", datetime="2013-10-05 22:00")
p(
"pay attention, because you'll find out that you can",
mark(' highlight', style={'backgroundColor': 'blue', 'color': 'white'}),
span(" to ", style={ 'backgroundColor': 'yellow' }),
" yo... | _____no_output_____ | BSD-3-Clause | example-notebooks/exploring-elements.ipynb | hussainsultan/vdom |
* 範例目標： 1. 實做 groupby 函式實現資料科學的 Split-Apply-Combine 策略* 範例重點： 1. Groupby：可以同時針對多個欄位做 Group，並在Group中做運算 2. Split：將大的數據集拆成可獨立計算的小數據集 3. Apply：獨立計算各個小數據集 4. Combine：將小數據集運算結果合併 | # 載入 NumPy, Pandas 套件
import numpy as np
import pandas as pd
# 檢查正確載入與版本
print(np)
print(np.__version__)
print(pd)
print(pd.__version__) | <module 'numpy' from 'D:\\anaconda3\\lib\\site-packages\\numpy\\__init__.py'>
1.19.2
<module 'pandas' from 'D:\\anaconda3\\lib\\site-packages\\pandas\\__init__.py'>
1.1.3
| MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
【基礎16=進階15】 | score_df = pd.DataFrame([[1,50,80,70,'boy'],
[2,60,45,50,'boy'],
[3,98,43,55,'boy'],
[4,70,69,89,'boy'],
[5,56,79,60,'girl'],
[6,60,68,55,'girl'],
[7,45,70,77,'girl'],
[8,55,77,76,'girl'],
[9,25,57,60,'girl'... | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
平均 * (法一)運用索引將資料分開 | boy_score_df = score_df.loc[score_df.sex=='boy']
girl_score_df = score_df.loc[score_df.sex=='girl']
print(boy_score_df.mean())
print(girl_score_df.mean()) | math_score 69.50
english_score 59.25
chinese_score 66.00
dtype: float64
math_score 54.833333
english_score 65.166667
chinese_score 61.833333
dtype: float64
| MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
* (法二)運用groupby方法 | score_df.groupby('sex').mean() | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
* 新增欄位class | score_df['class'] = [1,2,1,2,1,2,1,2,1,2]
score_df | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
Group By 對多個欄位做分析 * 寫法：你的 dataframe 變數名稱.groupby(['要分析之行的名稱', '可以多個']).運算函數名稱() * Split：將大的數據集拆成可獨立計算的小數據集，如：拆成男生、女生資料 * Apply：獨立計算各個小數據集，如成績取平均 * Combine：將小數據集運算結果合併 | score_df.groupby(['sex','class']).mean() | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
對欄位做多個分析 * 寫法：你的 dataframe 變數名稱.groupby(['要分析之行的名稱']).agg(['運算函數名稱','可以多個運算函數']) | score_df.groupby(['sex']).agg(['mean','std']) | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
對多個欄位做多個分析 * 寫法：你的 dataframe 變數名稱.groupby(['要分析之行的名稱','可以多個']).agg(['運算函數名稱','可以多個運算函數']) | score_df.groupby(['sex','class']).agg(['mean','max']) | _____no_output_____ | MIT | Sample/Day_16_Sample.ipynb | sueshow/Data_Science_Marathon |
Copyright 2019 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
Text generation with an RNNView on TensorFlow.org Run in Google ColabView source on GitHub This tutorial demonstrates how to generate text using a character-based RNN. We will work with a dataset of Shakespeare's writing from Andrej Karpathy's [The Unreasonable Effectiveness of Recurrent Neural Networks](http://kar... | from __future__ import absolute_import, division, print_function, unicode_literals
!pip install tensorflow-gpu==2.0.0-alpha0
import tensorflow as tf
import numpy as np
import os
import time | Collecting tensorflow-gpu==2.0.0-alpha0
Successfully installed google-pasta-0.1.4 tb-nightly-1.14.0a20190303 tensorflow-estimator-2.0-preview-1.14.0.dev2019030300 tensorflow-gpu==2.0.0-alpha0-2.0.0.dev20190303
| Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
Download the Shakespeare datasetChange the following line to run this code on your own data. | path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt') | Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt
1122304/1115394 [==============================] - 0s 0us/step
| Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
Read the dataFirst, look in the text: | # Read, then decode for py2 compat.
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# length of text is the number of characters in it
print ('Length of text: {} characters'.format(len(text)))
# Take a look at the first 250 characters in text
print(text[:250])
# The unique characters in the file
vocab =... | 65 unique characters
| Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
Process the text Vectorize the textBefore training, we need to map strings to a numerical representation. Create two lookup tables: one mapping characters to numbers, and another for numbers to characters. | # Creating a mapping from unique characters to indices
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text]) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
Now we have an integer representation for each character. Notice that we mapped the character as indexes from 0 to `len(unique)`. | print('{')
for char,_ in zip(char2idx, range(20)):
print(' {:4s}: {:3d},'.format(repr(char), char2idx[char]))
print(' ...\n}')
# Show how the first 13 characters from the text are mapped to integers
print ('{} ---- characters mapped to int ---- > {}'.format(repr(text[:13]), text_as_int[:13])) | 'First Citizen' ---- characters mapped to int ---- > [18 47 56 57 58 1 15 47 58 47 64 43 52]
| Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
The prediction task Given a character, or a sequence of characters, what is the most probable next character? This is the task we're training the model to perform. The input to the model will be a sequence of characters, and we train the model to predict the output—the following character at each time step.Since RNNs ... | # The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text)//seq_length
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
for i in char_dataset.take(5):
print(idx2char[i.numpy()]) | F
i
r
s
t
| Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
The `batch` method lets us easily convert these individual characters to sequences of the desired size. | sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
for item in sequences.take(5):
print(repr(''.join(idx2char[item.numpy()]))) | 'First Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citizen:\nYou '
'are all resolved rather to die than to famish?\n\nAll:\nResolved. resolved.\n\nFirst Citizen:\nFirst, you k'
"now Caius Marcius is chief enemy to the people.\n\nAll:\nWe know't, we know't.\n\nFirst Citizen:\n... | Apache-2.0 | site/en/r2/tutorials/text/text_generation.ipynb | mullikine/tfdocs |
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