欢迎来到一个教程，在这里我们将讨论卷积神经网络(Convnet和CNN)，使用其中的一个用我们在上一教程中构建的数据集对狗和猫进行分类。

卷积神经网络通过它在图像数据中的应用而获得了广泛的应用，并且是目前检测图像内容或包含在图像中的最先进的技术。

CNN的基本结构如下：Convolution -> Pooling -> Convolution -> Pooling -> Fully Connected Layer -> Output

Convolution获取原始数据并从中创建功能地图的行为。Pooling是下采样，最常见的形式是“最大池”，我们选择一个区域，然后取该区域的最大值，这将成为整个区域的新值。Fully Connected Layers是典型的神经网络，所有的节点都“完全连接”。卷积层没有像传统的神经网络那样完全连接起来。

好吧，现在让我们来描述一下发生了什么。我们将从一只猫的图像开始：
卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
然后“转换为像素：”卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
为了本教程的目的，假设每个正方形都是一个像素。接下来，对于卷积步骤，我们将取一个特定的窗口，并在该窗口中找到特性：卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
该窗口的功能现在只是一个像素大小的新功能地图，但我们将有多层的功能地图在现实中。

接下来，我们滑过这个窗口并继续这个过程。会有一些重叠，你可以确定你想要多少，你只是不想跳过任何像素，当然卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
现在，您继续这个过程，直到覆盖了整个图像，然后您将有一个功能地图。通常，特征图只是更多的像素值，只是一个非常简单的值：
卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
从这里开始我们要集中精力。比方说，我们的卷积给了我们(我忘了在第二行的最右方格中放一个数字，假设它是3或更少)：卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
现在我们将使用一个3x3池窗口：卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
最常见的池形式是“最大池”，在这里我们简单地取窗口中的最大值，这将成为该区域的新值。
卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
我们继续这个过程，直到我们集合起来，并且有如下内容：
卷积神经网络-Python、TensorFlow和Keras p.3的深度学习基础
每个卷积和池步骤都是一个隐藏层。在此之后，我们有一个完全连接的层，然后是输出层。完全连接层是典型的神经网络(多层感知器)类型，与输出层相同。

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D

import pickle

pickle_in = open(“X.pickle”,“rb”)
X = pickle.load(pickle_in)

pickle_in = open(“y.pickle”,“rb”)
y = pickle.load(pickle_in)

X = X/255.0

model = Sequential()

model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(256, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Dense(1))
model.add(Activation(‘sigmoid’))

model.compile(loss=‘binary_crossentropy’,
optimizer=‘adam’,
metrics=[‘accuracy’])

model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)
Train on 17441 samples, validate on 7475 samples
Epoch 1/3
17441/17441 [] - ETA: 9:04 - loss: 0.6950 - acc: 0.500 - ETA: 1:35 - loss: 0.7981 - acc: 0.500 - ETA: 54s - loss: 0.7450 - acc: 0.542 - ETA: 38s - loss: 0.7328 - acc: 0.54 - ETA: 30s - loss: 0.7235 - acc: 0.53 - ETA: 25s - loss: 0.7176 - acc: 0.53 - ETA: 22s - loss: 0.7137 - acc: 0.52 - ETA: 19s - loss: 0.7107 - acc: 0.52 - ETA: 17s - loss: 0.7085 - acc: 0.52 - ETA: 16s - loss: 0.7065 - acc: 0.52 - ETA: 15s - loss: 0.7051 - acc: 0.52 - ETA: 14s - loss: 0.7033 - acc: 0.52 - ETA: 13s - loss: 0.7021 - acc: 0.52 - ETA: 12s - loss: 0.7004 - acc: 0.52 - ETA: 11s - loss: 0.6996 - acc: 0.52 - ETA: 11s - loss: 0.6983 - acc: 0.53 - ETA: 10s - loss: 0.6978 - acc: 0.53 - ETA: 10s - loss: 0.6977 - acc: 0.53 - ETA: 10s - loss: 0.6968 - acc: 0.54 - ETA: 9s - loss: 0.6957 - acc: 0.5469 - ETA: 9s - loss: 0.6957 - acc: 0.545 - ETA: 9s - loss: 0.6943 - acc: 0.549 - ETA: 8s - loss: 0.6928 - acc: 0.552 - ETA: 8s - loss: 0.6923 - acc: 0.554 - ETA: 8s - loss: 0.6922 - acc: 0.553 - ETA: 8s - loss: 0.6918 - acc: 0.554 - ETA: 7s - loss: 0.6913 - acc: 0.556 - ETA: 7s - loss: 0.6915 - acc: 0.554 - ETA: 7s - loss: 0.6902 - acc: 0.554 - ETA: 7s - loss: 0.6901 - acc: 0.553 - ETA: 7s - loss: 0.6893 - acc: 0.555 - ETA: 6s - loss: 0.6886 - acc: 0.557 - ETA: 6s - loss: 0.6881 - acc: 0.556 - ETA: 6s - loss: 0.6866 - acc: 0.558 - ETA: 6s - loss: 0.6868 - acc: 0.559 - ETA: 6s - loss: 0.6867 - acc: 0.559 - ETA: 6s - loss: 0.6866 - acc: 0.559 - ETA: 5s - loss: 0.6863 - acc: 0.559 - ETA: 5s - loss: 0.6853 - acc: 0.561 - ETA: 5s - loss: 0.6845 - acc: 0.562 - ETA: 5s - loss: 0.6844 - acc: 0.562 - ETA: 5s - loss: 0.6823 - acc: 0.565 - ETA: 5s - loss: 0.6817 - acc: 0.567 - ETA: 5s - loss: 0.6817 - acc: 0.568 - ETA: 5s - loss: 0.6813 - acc: 0.569 - ETA: 5s - loss: 0.6812 - acc: 0.569 - ETA: 4s - loss: 0.6812 - acc: 0.570 - ETA: 4s - loss: 0.6806 - acc: 0.571 - ETA: 4s - loss: 0.6798 - acc: 0.572 - ETA: 4s - loss: 0.6789 - acc: 0.573 - ETA: 4s - loss: 0.6783 - acc: 0.574 - ETA: 4s - loss: 0.6774 - acc: 0.575 - ETA: 4s - loss: 0.6770 - acc: 0.575 - ETA: 4s - loss: 0.6770 - acc: 0.574 - ETA: 4s - loss: 0.6766 - acc: 0.575 - ETA: 4s - loss: 0.6762 - acc: 0.576 - ETA: 3s - loss: 0.6758 - acc: 0.577 - ETA: 3s - loss: 0.6753 - acc: 0.578 - ETA: 3s - loss: 0.6750 - acc: 0.579 - ETA: 3s - loss: 0.6750 - acc: 0.579 - ETA: 3s - loss: 0.6742 - acc: 0.580 - ETA: 3s - loss: 0.6737 - acc: 0.581 - ETA: 3s - loss: 0.6731 - acc: 0.582 - ETA: 3s - loss: 0.6734 - acc: 0.581 - ETA: 3s - loss: 0.6729 - acc: 0.582 - ETA: 3s - loss: 0.6720 - acc: 0.583 - ETA: 3s - loss: 0.6718 - acc: 0.583 - ETA: 2s - loss: 0.6710 - acc: 0.585 - ETA: 2s - loss: 0.6712 - acc: 0.585 - ETA: 2s - loss: 0.6712 - acc: 0.585 - ETA: 2s - loss: 0.6710 - acc: 0.586 - ETA: 2s - loss: 0.6706 - acc: 0.586 - ETA: 2s - loss: 0.6706 - acc: 0.587 - ETA: 2s - loss: 0.6708 - acc: 0.587 - ETA: 2s - loss: 0.6704 - acc: 0.587 - ETA: 2s - loss: 0.6709 - acc: 0.587 - ETA: 2s - loss: 0.6710 - acc: 0.586 - ETA: 2s - loss: 0.6709 - acc: 0.587 - ETA: 2s - loss: 0.6710 - acc: 0.587 - ETA: 2s - loss: 0.6710 - acc: 0.586 - ETA: 1s - loss: 0.6704 - acc: 0.587 - ETA: 1s - loss: 0.6703 - acc: 0.587 - ETA: 1s - loss: 0.6701 - acc: 0.587 - ETA: 1s - loss: 0.6695 - acc: 0.589 - ETA: 1s - loss: 0.6695 - acc: 0.589 - ETA: 1s - loss: 0.6693 - acc: 0.588 - ETA: 1s - loss: 0.6695 - acc: 0.589 - ETA: 1s - loss: 0.6691 - acc: 0.589 - ETA: 1s - loss: 0.6690 - acc: 0.590 - ETA: 1s - loss: 0.6690 - acc: 0.590 - ETA: 1s - loss: 0.6687 - acc: 0.590 - ETA: 1s - loss: 0.6685 - acc: 0.591 - ETA: 1s - loss: 0.6683 - acc: 0.591 - ETA: 1s - loss: 0.6684 - acc: 0.591 - ETA: 1s - loss: 0.6682 - acc: 0.592 - ETA: 0s - loss: 0.6680 - acc: 0.592 - ETA: 0s - loss: 0.6678 - acc: 0.592 - ETA: 0s - loss: 0.6679 - acc: 0.592 - ETA: 0s - loss: 0.6680 - acc: 0.592 - ETA: 0s - loss: 0.6676 - acc: 0.593 - ETA: 0s - loss: 0.6669 - acc: 0.594 - ETA: 0s - loss: 0.6667 - acc: 0.595 - ETA: 0s - loss: 0.6660 - acc: 0.596 - ETA: 0s - loss: 0.6665 - acc: 0.596 - ETA: 0s - loss: 0.6661 - acc: 0.597 - ETA: 0s - loss: 0.6658 - acc: 0.597 - ETA: 0s - loss: 0.6652 - acc: 0.598 - ETA: 0s - loss: 0.6644 - acc: 0.599 - ETA: 0s - loss: 0.6643 - acc: 0.600 - ETA: 0s - loss: 0.6639 - acc: 0.600 - 8s 467us/step - loss: 0.6639 - acc: 0.6005 - val_loss: 0.6386 - val_acc: 0.6384
Epoch 2/3
17441/17441 [] - ETA: 5s - loss: 0.7182 - acc: 0.468 - ETA: 6s - loss: 0.6551 - acc: 0.567 - ETA: 6s - loss: 0.6405 - acc: 0.605 - ETA: 6s - loss: 0.6279 - acc: 0.636 - ETA: 5s - loss: 0.6330 - acc: 0.633 - ETA: 5s - loss: 0.6263 - acc: 0.643 - ETA: 5s - loss: 0.6318 - acc: 0.642 - ETA: 5s - loss: 0.6328 - acc: 0.637 - ETA: 5s - loss: 0.6350 - acc: 0.635 - ETA: 5s - loss: 0.6364 - acc: 0.635 - ETA: 5s - loss: 0.6352 - acc: 0.634 - ETA: 5s - loss: 0.6366 - acc: 0.628 - ETA: 5s - loss: 0.6345 - acc: 0.632 - ETA: 5s - loss: 0.6274 - acc: 0.644 - ETA: 5s - loss: 0.6289 - acc: 0.644 - ETA: 5s - loss: 0.6273 - acc: 0.645 - ETA: 5s - loss: 0.6285 - acc: 0.644 - ETA: 5s - loss: 0.6284 - acc: 0.645 - ETA: 5s - loss: 0.6291 - acc: 0.644 - ETA: 5s - loss: 0.6334 - acc: 0.640 - ETA: 5s - loss: 0.6333 - acc: 0.640 - ETA: 4s - loss: 0.6327 - acc: 0.644 - ETA: 4s - loss: 0.6335 - acc: 0.642 - ETA: 4s - loss: 0.6347 - acc: 0.642 - ETA: 4s - loss: 0.6354 - acc: 0.642 - ETA: 4s - loss: 0.6358 - acc: 0.642 - ETA: 4s - loss: 0.6367 - acc: 0.640 - ETA: 4s - loss: 0.6360 - acc: 0.640 - ETA: 4s - loss: 0.6341 - acc: 0.642 - ETA: 4s - loss: 0.6337 - acc: 0.641 - ETA: 4s - loss: 0.6329 - acc: 0.644 - ETA: 4s - loss: 0.6309 - acc: 0.646 - ETA: 4s - loss: 0.6317 - acc: 0.644 - ETA: 4s - loss: 0.6306 - acc: 0.646 - ETA: 4s - loss: 0.6300 - acc: 0.646 - ETA: 4s - loss: 0.6293 - acc: 0.646 - ETA: 4s - loss: 0.6296 - acc: 0.645 - ETA: 4s - loss: 0.6277 - acc: 0.648 - ETA: 4s - loss: 0.6284 - acc: 0.647 - ETA: 3s - loss: 0.6270 - acc: 0.650 - ETA: 3s - loss: 0.6269 - acc: 0.650 - ETA: 3s - loss: 0.6272 - acc: 0.650 - ETA: 3s - loss: 0.6275 - acc: 0.650 - ETA: 3s - loss: 0.6273 - acc: 0.650 - ETA: 3s - loss: 0.6258 - acc: 0.651 - ETA: 3s - loss: 0.6259 - acc: 0.651 - ETA: 3s - loss: 0.6258 - acc: 0.651 - ETA: 3s - loss: 0.6244 - acc: 0.653 - ETA: 3s - loss: 0.6244 - acc: 0.653 - ETA: 3s - loss: 0.6246 - acc: 0.654 - ETA: 3s - loss: 0.6237 - acc: 0.655 - ETA: 3s - loss: 0.6233 - acc: 0.656 - ETA: 3s - loss: 0.6240 - acc: 0.655 - ETA: 3s - loss: 0.6229 - acc: 0.656 - ETA: 3s - loss: 0.6228 - acc: 0.656 - ETA: 3s - loss: 0.6229 - acc: 0.656 - ETA: 3s - loss: 0.6237 - acc: 0.656 - ETA: 2s - loss: 0.6236 - acc: 0.656 - ETA: 2s - loss: 0.6231 - acc: 0.656 - ETA: 2s - loss: 0.6234 - acc: 0.656 - ETA: 2s - loss: 0.6234 - acc: 0.656 - ETA: 2s - loss: 0.6238 - acc: 0.655 - ETA: 2s - loss: 0.6226 - acc: 0.657 - ETA: 2s - loss: 0.6231 - acc: 0.657 - ETA: 2s - loss: 0.6234 - acc: 0.657 - ETA: 2s - loss: 0.6228 - acc: 0.658 - ETA: 2s - loss: 0.6207 - acc: 0.660 - ETA: 2s - loss: 0.6201 - acc: 0.660 - ETA: 2s - loss: 0.6200 - acc: 0.660 - ETA: 2s - loss: 0.6196 - acc: 0.661 - ETA: 2s - loss: 0.6190 - acc: 0.661 - ETA: 2s - loss: 0.6178 - acc: 0.663 - ETA: 2s - loss: 0.6180 - acc: 0.662 - ETA: 2s - loss: 0.6175 - acc: 0.662 - ETA: 1s - loss: 0.6174 - acc: 0.662 - ETA: 1s - loss: 0.6174 - acc: 0.663 - ETA: 1s - loss: 0.6174 - acc: 0.663 - ETA: 1s - loss: 0.6163 - acc: 0.664 - ETA: 1s - loss: 0.6151 - acc: 0.666 - ETA: 1s - loss: 0.6143 - acc: 0.666 - ETA: 1s - loss: 0.6133 - acc: 0.667 - ETA: 1s - loss: 0.6139 - acc: 0.667 - ETA: 1s - loss: 0.6143 - acc: 0.667 - ETA: 1s - loss: 0.6139 - acc: 0.667 - ETA: 1s - loss: 0.6142 - acc: 0.666 - ETA: 1s - loss: 0.6141 - acc: 0.666 - ETA: 1s - loss: 0.6138 - acc: 0.666 - ETA: 1s - loss: 0.6131 - acc: 0.667 - ETA: 1s - loss: 0.6122 - acc: 0.668 - ETA: 1s - loss: 0.6122 - acc: 0.668 - ETA: 1s - loss: 0.6113 - acc: 0.669 - ETA: 1s - loss: 0.6107 - acc: 0.670 - ETA: 0s - loss: 0.6109 - acc: 0.670 - ETA: 0s - loss: 0.6103 - acc: 0.671 - ETA: 0s - loss: 0.6109 - acc: 0.670 - ETA: 0s - loss: 0.6103 - acc: 0.671 - ETA: 0s - loss: 0.6099 - acc: 0.672 - ETA: 0s - loss: 0.6094 - acc: 0.672 - ETA: 0s - loss: 0.6089 - acc: 0.672 - ETA: 0s - loss: 0.6086 - acc: 0.672 - ETA: 0s - loss: 0.6096 - acc: 0.671 - ETA: 0s - loss: 0.6094 - acc: 0.671 - ETA: 0s - loss: 0.6092 - acc: 0.671 - ETA: 0s - loss: 0.6087 - acc: 0.672 - ETA: 0s - loss: 0.6084 - acc: 0.672 - ETA: 0s - loss: 0.6079 - acc: 0.673 - ETA: 0s - loss: 0.6075 - acc: 0.673 - ETA: 0s - loss: 0.6074 - acc: 0.674 - ETA: 0s - loss: 0.6064 - acc: 0.674 - 7s 404us/step - loss: 0.6059 - acc: 0.6749 - val_loss: 0.5673 - val_acc: 0.7025
Epoch 3/3
17441/17441 [==============================] - ETA: 5s - loss: 0.5591 - acc: 0.625 - ETA: 6s - loss: 0.5442 - acc: 0.729 - ETA: 6s - loss: 0.5434 - acc: 0.730 - ETA: 5s - loss: 0.5347 - acc: 0.732 - ETA: 5s - loss: 0.5355 - acc: 0.730 - ETA: 5s - loss: 0.5372 - acc: 0.735 - ETA: 5s - loss: 0.5334 - acc: 0.737 - ETA: 5s - loss: 0.5361 - acc: 0.730 - ETA: 5s - loss: 0.5282 - acc: 0.735 - ETA: 5s - loss: 0.5292 - acc: 0.732 - ETA: 5s - loss: 0.5319 - acc: 0.730 - ETA: 5s - loss: 0.5327 - acc: 0.731 - ETA: 5s - loss: 0.5315 - acc: 0.732 - ETA: 5s - loss: 0.5290 - acc: 0.736 - ETA: 5s - loss: 0.5291 - acc: 0.736 - ETA: 5s - loss: 0.5341 - acc: 0.737 - ETA: 5s - loss: 0.5378 - acc: 0.734 - ETA: 5s - loss: 0.5368 - acc: 0.735 - ETA: 5s - loss: 0.5366 - acc: 0.734 - ETA: 5s - loss: 0.5373 - acc: 0.733 - ETA: 5s - loss: 0.5383 - acc: 0.730 - ETA: 4s - loss: 0.5424 - acc: 0.729 - ETA: 4s - loss: 0.5414 - acc: 0.730 - ETA: 4s - loss: 0.5426 - acc: 0.728 - ETA: 4s - loss: 0.5416 - acc: 0.729 - ETA: 4s - loss: 0.5428 - acc: 0.729 - ETA: 4s - loss: 0.5423 - acc: 0.728 - ETA: 4s - loss: 0.5431 - acc: 0.729 - ETA: 4s - loss: 0.5442 - acc: 0.727 - ETA: 4s - loss: 0.5447 - acc: 0.726 - ETA: 4s - loss: 0.5437 - acc: 0.727 - ETA: 4s - loss: 0.5424 - acc: 0.729 - ETA: 4s - loss: 0.5434 - acc: 0.728 - ETA: 4s - loss: 0.5421 - acc: 0.729 - ETA: 4s - loss: 0.5423 - acc: 0.728 - ETA: 4s - loss: 0.5429 - acc: 0.727 - ETA: 4s - loss: 0.5407 - acc: 0.728 - ETA: 4s - loss: 0.5415 - acc: 0.728 - ETA: 4s - loss: 0.5423 - acc: 0.728 - ETA: 3s - loss: 0.5412 - acc: 0.729 - ETA: 3s - loss: 0.5416 - acc: 0.728 - ETA: 3s - loss: 0.5410 - acc: 0.729 - ETA: 3s - loss: 0.5414 - acc: 0.730 - ETA: 3s - loss: 0.5403 - acc: 0.730 - ETA: 3s - loss: 0.5401 - acc: 0.729 - ETA: 3s - loss: 0.5401 - acc: 0.729 - ETA: 3s - loss: 0.5408 - acc: 0.728 - ETA: 3s - loss: 0.5419 - acc: 0.728 - ETA: 3s - loss: 0.5413 - acc: 0.729 - ETA: 3s - loss: 0.5409 - acc: 0.729 - ETA: 3s - loss: 0.5404 - acc: 0.731 - ETA: 3s - loss: 0.5402 - acc: 0.730 - ETA: 3s - loss: 0.5406 - acc: 0.730 - ETA: 3s - loss: 0.5412 - acc: 0.730 - ETA: 3s - loss: 0.5418 - acc: 0.729 - ETA: 3s - loss: 0.5420 - acc: 0.728 - ETA: 2s - loss: 0.5417 - acc: 0.729 - ETA: 2s - loss: 0.5425 - acc: 0.728 - ETA: 2s - loss: 0.5430 - acc: 0.728 - ETA: 2s - loss: 0.5428 - acc: 0.728 - ETA: 2s - loss: 0.5425 - acc: 0.728 - ETA: 2s - loss: 0.5419 - acc: 0.729 - ETA: 2s - loss: 0.5422 - acc: 0.729 - ETA: 2s - loss: 0.5422 - acc: 0.729 - ETA: 2s - loss: 0.5429 - acc: 0.729 - ETA: 2s - loss: 0.5436 - acc: 0.729 - ETA: 2s - loss: 0.5436 - acc: 0.729 - ETA: 2s - loss: 0.5442 - acc: 0.728 - ETA: 2s - loss: 0.5435 - acc: 0.728 - ETA: 2s - loss: 0.5431 - acc: 0.729 - ETA: 2s - loss: 0.5426 - acc: 0.729 - ETA: 2s - loss: 0.5425 - acc: 0.729 - ETA: 2s - loss: 0.5417 - acc: 0.730 - ETA: 2s - loss: 0.5407 - acc: 0.731 - ETA: 1s - loss: 0.5413 - acc: 0.730 - ETA: 1s - loss: 0.5413 - acc: 0.730 - ETA: 1s - loss: 0.5423 - acc: 0.730 - ETA: 1s - loss: 0.5420 - acc: 0.730 - ETA: 1s - loss: 0.5425 - acc: 0.730 - ETA: 1s - loss: 0.5426 - acc: 0.730 - ETA: 1s - loss: 0.5425 - acc: 0.730 - ETA: 1s - loss: 0.5419 - acc: 0.730 - ETA: 1s - loss: 0.5426 - acc: 0.729 - ETA: 1s - loss: 0.5434 - acc: 0.728 - ETA: 1s - loss: 0.5432 - acc: 0.728 - ETA: 1s - loss: 0.5428 - acc: 0.729 - ETA: 1s - loss: 0.5428 - acc: 0.729 - ETA: 1s - loss: 0.5433 - acc: 0.728 - ETA: 1s - loss: 0.5430 - acc: 0.729 - ETA: 1s - loss: 0.5434 - acc: 0.729 - ETA: 1s - loss: 0.5428 - acc: 0.729 - ETA: 1s - loss: 0.5424 - acc: 0.730 - ETA: 0s - loss: 0.5423 - acc: 0.730 - ETA: 0s - loss: 0.5426 - acc: 0.730 - ETA: 0s - loss: 0.5420 - acc: 0.730 - ETA: 0s - loss: 0.5414 - acc: 0.731 - ETA: 0s - loss: 0.5417 - acc: 0.731 - ETA: 0s - loss: 0.5422 - acc: 0.730 - ETA: 0s - loss: 0.5418 - acc: 0.731 - ETA: 0s - loss: 0.5413 - acc: 0.731 - ETA: 0s - loss: 0.5412 - acc: 0.731 - ETA: 0s - loss: 0.5412 - acc: 0.731 - ETA: 0s - loss: 0.5406 - acc: 0.732 - ETA: 0s - loss: 0.5404 - acc: 0.732 - ETA: 0s - loss: 0.5397 - acc: 0.732 - ETA: 0s - loss: 0.5396 - acc: 0.732 - ETA: 0s - loss: 0.5388 - acc: 0.733 - ETA: 0s - loss: 0.5383 - acc: 0.733 - ETA: 0s - loss: 0.5389 - acc: 0.733 - 7s 404us/step - loss: 0.5385 - acc: 0.7339 - val_loss: 0.5626 - val_acc: 0.7171
<tensorflow.python.keras.callbacks.History at 0x195889c1898>
经过仅仅三个时代，我们有71%的验证准确性。如果我们继续前进，我们可能会做得更好，但我们应该讨论我们是如何知道我们是如何做的。为了帮助这一点，我们可以使用TensorBoard，它附带了TensorFlow，它帮助您可视化您的模型，因为他们是经过培训的。

我们将在下一篇教程中讨论TensorBoard以及对我们的模型的各种调整！

下一个教程：使用Tensorboard分析模型-使用Python、TensorFlow和Keras P.4深入学习基础知识