06_ConvolutionalNeuralNetwork-Hoda-Keras.ipynb
آشنایی با سرویس ابری Google Colab
اتصال مستقیم سرویس کولب (Google Colab) به درایو (Google Drive) از طریق فایل سیستم FUSE
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.layers import Conv2D, MaxPooling2D
from keras.datasets import cifar10
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Using TensorFlow backend.
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
plt.imshow(x_train[7])
<matplotlib.image.AxesImage at 0x2800004ab38>
x_train =
x_test =
y_train[0:10]
array([[6], [9], [9], [4], [1], [1], [2], [7], [8], [3]], dtype=uint8)
y_train =
y_test =
y_train[0:10]
array([[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.], [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.], [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]], dtype=float32)
opt_rms = keras.optimizers.RMSprop(learning_rate=0.001,decay=1e-6)
Train on 50000 samples, validate on 10000 samples Epoch 1/25 50000/50000 [==============================] - 16s 314us/step - loss: 1.8149 - acc: 0.3340 - val_loss: 1.4958 - val_acc: 0.4657 Epoch 2/25 50000/50000 [==============================] - 12s 238us/step - loss: 1.4985 - acc: 0.4576 - val_loss: 1.4273 - val_acc: 0.4949 Epoch 3/25 50000/50000 [==============================] - 13s 267us/step - loss: 1.3704 - acc: 0.5097 - val_loss: 1.3056 - val_acc: 0.5215 Epoch 4/25 50000/50000 [==============================] - 12s 240us/step - loss: 1.2872 - acc: 0.5429 - val_loss: 1.3538 - val_acc: 0.5080 Epoch 5/25 50000/50000 [==============================] - 12s 240us/step - loss: 1.2318 - acc: 0.5670 - val_loss: 1.1812 - val_acc: 0.5825 Epoch 6/25 50000/50000 [==============================] - 12s 244us/step - loss: 1.1888 - acc: 0.5802 - val_loss: 1.2271 - val_acc: 0.5912 Epoch 7/25 50000/50000 [==============================] - 12s 241us/step - loss: 1.1481 - acc: 0.5959 - val_loss: 1.1042 - val_acc: 0.6139 Epoch 8/25 50000/50000 [==============================] - 12s 240us/step - loss: 1.1248 - acc: 0.6041 - val_loss: 1.1360 - val_acc: 0.5956 Epoch 9/25 50000/50000 [==============================] - 12s 243us/step - loss: 1.0937 - acc: 0.6126 - val_loss: 1.0235 - val_acc: 0.6406 Epoch 10/25 50000/50000 [==============================] - 12s 241us/step - loss: 1.0750 - acc: 0.6232 - val_loss: 1.0030 - val_acc: 0.6523 Epoch 11/25 50000/50000 [==============================] - 12s 240us/step - loss: 1.0486 - acc: 0.6329 - val_loss: 1.0376 - val_acc: 0.6421 Epoch 12/25 50000/50000 [==============================] - 12s 247us/step - loss: 1.0317 - acc: 0.6393 - val_loss: 1.0864 - val_acc: 0.6116 Epoch 13/25 50000/50000 [==============================] - 13s 260us/step - loss: 1.0181 - acc: 0.6450 - val_loss: 0.9765 - val_acc: 0.6542 Epoch 14/25 50000/50000 [==============================] - 13s 250us/step - loss: 1.0019 - acc: 0.6493 - val_loss: 0.9678 - val_acc: 0.6597 Epoch 15/25 50000/50000 [==============================] - 12s 230us/step - loss: 0.9912 - acc: 0.6530 - val_loss: 0.9826 - val_acc: 0.6629 Epoch 16/25 50000/50000 [==============================] - 12s 245us/step - loss: 0.9827 - acc: 0.6549 - val_loss: 1.1734 - val_acc: 0.6110 Epoch 17/25 50000/50000 [==============================] - 12s 240us/step - loss: 0.9739 - acc: 0.6601 - val_loss: 1.1113 - val_acc: 0.6096 Epoch 18/25 50000/50000 [==============================] - 12s 232us/step - loss: 0.9639 - acc: 0.6637 - val_loss: 0.9663 - val_acc: 0.6651 Epoch 19/25 50000/50000 [==============================] - 12s 231us/step - loss: 0.9565 - acc: 0.6670 - val_loss: 0.9510 - val_acc: 0.6765 Epoch 20/25 50000/50000 [==============================] - 11s 229us/step - loss: 0.9474 - acc: 0.6724 - val_loss: 1.0058 - val_acc: 0.6634 Epoch 21/25 50000/50000 [==============================] - 12s 230us/step - loss: 0.9327 - acc: 0.6719 - val_loss: 0.9580 - val_acc: 0.6683 Epoch 22/25 50000/50000 [==============================] - 12s 230us/step - loss: 0.9333 - acc: 0.6719 - val_loss: 0.8680 - val_acc: 0.7061 Epoch 23/25 50000/50000 [==============================] - 11s 229us/step - loss: 0.9240 - acc: 0.6770 - val_loss: 0.9933 - val_acc: 0.6646 Epoch 24/25 50000/50000 [==============================] - 11s 229us/step - loss: 0.9215 - acc: 0.6785 - val_loss: 0.9356 - val_acc: 0.6790 Epoch 25/25 50000/50000 [==============================] - 11s 228us/step - loss: 0.9164 - acc: 0.6819 - val_loss: 0.9316 - val_acc: 0.6818
<keras.callbacks.History at 0x280000aea20>