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from keras.datasets import cifar10 | |
import matplotlib.pyplot as plt | |
(X_train, y_train), (X_test, y_test) = cifar10.load_data() | |
plt.figure(facecolor='white') | |
for i in range(100): | |
plt.subplot(10, 10, i+1) | |
plt.imshow(X_train[i]) |
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# Import library in Keras 1.2.0 | |
from keras.layers.recurrent import SimpleRNN, GRU, LSTM | |
from keras.models import Sequential | |
from keras.layers import Dense, Activation | |
from keras.callbacks import EarlyStopping | |
from keras.utils.visualize_util import plot | |
# Define parameters | |
HIDDEN_SIZE = 128 | |
BATCH_SIZE = 10 |
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train( [60, 67, 63, 60, 65, 62, 60, 67, 63, 60] ) -> label( 65 ) | |
train( [67, 63, 60, 65, 62, 60, 67, 63, 60, 65] ) -> label( 62 ) | |
train( [63, 60, 65, 62, 60, 67, 63, 60, 65, 62] ) -> label( 60 ) | |
train( [60, 65, 62, 60, 67, 63, 60, 65, 62, 60] ) -> label( 67 ) | |
train( [65, 62, 60, 67, 63, 60, 65, 62, 60, 67] ) -> label( 63 ) | |
train( [62, 60, 67, 63, 60, 65, 62, 60, 67, 63] ) -> label( 60 ) | |
train( [60, 67, 63, 60, 65, 62, 60, 67, 63, 60] ) -> label( 65 ) | |
train( [67, 63, 60, 65, 62, 60, 67, 63, 60, 65] ) -> label( 62 ) | |
train( [63, 60, 65, 62, 60, 67, 63, 60, 65, 62] ) -> label( 60 ) | |
train( [60, 65, 62, 60, 67, 63, 60, 65, 62, 60] ) -> label( 67 ) |
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60, 67, 63, 60, 65, 62, 60, 67, 63, 60, 65, 62, 60, 67, 63, 60, 65, 62, 60, 67, ... |
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_________________________________________________________________ | |
Layer (type) Output Shape Param # | |
================================================================= | |
dense_1 (Dense) (None, 32) 16032 | |
_________________________________________________________________ | |
activation_1 (Activation) (None, 32) 0 | |
_________________________________________________________________ | |
dense_2 (Dense) (None, 32) 1056 | |
_________________________________________________________________ | |
activation_2 (Activation) (None, 32) 0 |
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MLP (3 layers, 32 neurons, 30 epochs) ... | |
accuracy = 0.9986 time = 14.8910531305 | |
[[3378 0 0] | |
[ 0 3407 0] | |
[ 13 1 3201]] | |
Naive Bayes ... | |
accuracy = 0.9874 time = 1.30055759897 | |
[[3252 0 126] |
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Train on 9000 samples, validate on 1000 samples | |
Epoch 1/15 | |
9000/9000 [==============================] - 1865s 207ms/step - loss: 0.6278 - acc: 0.6629 - val_loss: 0.5947 - val_acc: 0.6845 | |
Epoch 2/15 | |
9000/9000 [==============================] - 1810s 201ms/step - loss: 0.5216 - acc: 0.7424 - val_loss: 0.4472 - val_acc: 0.7916 | |
Epoch 3/15 | |
9000/9000 [==============================] - 1789s 199ms/step - loss: 0.4024 - acc: 0.8191 - val_loss: 0.3396 - val_acc: 0.8529 | |
Epoch 4/15 | |
9000/9000 [==============================] - 1817s 202ms/step - loss: 0.3204 - acc: 0.8646 - val_loss: 0.3059 - val_acc: 0.8740 | |
Epoch 5/15 |
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MNIST | CIFAR | Fashion | Dog/Cat | Shape | Fruit | Distracted Driver | Hand Gesture | |||
---|---|---|---|---|---|---|---|---|---|---|
Source | Keras | Keras | Keras | Home-Made | ? | ? | ? | ? | ||
Total Size | ? | ? | ? | ? | ? | ? | ? | ? | ||
Number of Image Files (total) | 70000 | 60000 | 70000 | ? | ? | ? | ? | ? | ||
Number of Image Files (training) | 60000 | 50000 | 60000 | ? | ? | ? | ? | ? | ||
Number of Image files (testing) | 10000 | 10000 | 10000 | ? | ? | ? | ? | ? | ||
Number of Classes | 10 | 10/100 | 10 | 2 | ? | ? | ? | ? | ||
Image Files per Class | 7000 | 6000/600 | 7000 | >1000 | ? | ? | ? | ? | ||
Size per Image File (max/min) | 28x28 | 32x32 | 28x28 | ? | ? | ? | ? | ? | ||
Format of Image File | numpy | numpy | numpy | ? | ? | ? | ? | ? |
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Cat/Dog Audio | British Birdsong | Heartbeat | Music Genre | Urban Sound | |||
---|---|---|---|---|---|---|---|
Source | Kaggle | Kaggle | Kaggle | Marsyas | UrbanSoundDataset | ||
Total Size | 49 MB | 633 MB | 111 MB | 1 GB | 6 GB | ||
Number of Audio Files (total) | 277 | 264 | ? | 1000 | 8732 | ||
Number of Audio Files (training) | - | - | ? | - | - | ||
Number of Audio files (testing) | - | - | ? | - | - | ||
Number of Classes | 2 | 88 | ? | 10 | 10 | ||
Audio Files per Class | cat (164) | 3 | ? | blues (100) | air_conditioner (1000) | ||
dog (113) | classical (100) | children_playing (1000) | |||||
country (100) | dog_bark (1000) |
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Cat/Dog Audio | British Birdsong | Music Genre | Urban Sound | |||
---|---|---|---|---|---|---|
Source | Kaggle | Kaggle | Marsyas | UrbanSoundDataset | ||
Total Size | 49 MB | 633 MB | 1 GB | 6 GB | ||
Number of Audio Files (total) | 277 | 264 | 1000 | 8732 | ||
Number of Audio Files (training) | - | - | - | 5435* | ||
Number of Audio files (testing) | - | - | - | 3297* | ||
Number of Classes | 2 | 88 | 10 | 10 | ||
Audio Files per Class | cat (164) | 3 | blues (100) | air_conditioner (1000) | ||
dog (113) | classical (100) | children_playing (1000) | ||||
country (100) | dog_bark (1000) |
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