深度学习之卷积模型应用

声明

本文参考Deep-Learning-Specialization-Coursera/Convolution_model_Application.ipynb at main · abdur75648/Deep-Learning-Specialization-Coursera · GitHub,力求理解。

资料下载

链接:https://pan.baidu.com/s/1xANUtgIWgt7gcul6dnInhA
提取码:oajj,请在开始之前下载好所需资料

1 – Packages

首先,我们要导入一些库

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
from matplotlib.pyplot import imread
import scipy
from PIL import Image
import pandas as pd
import tensorflow as tf
import tensorflow.keras.layers as tfl
from tensorflow.python.framework import ops
from cnn_utils import *
from test_utils import summary, comparator

%matplotlib inline
np.random.seed(1)

数据集我们使用的是 Happy House dataset

加载一下数据集吧!

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_happy_dataset()

# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.

# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.T

print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 600
number of test examples = 150
X_train shape: (600, 64, 64, 3)
Y_train shape: (600, 1)
X_test shape: (150, 64, 64, 3)
Y_test shape: (150, 1)
结果发现我们使用的是6464,3通道的图片
我们随机的看张图片
index = 120
plt.imshow(X_train_orig[index]) #display sample training image
plt.show()

《深度学习之卷积模型应用》

happyModel

Fully-connected (Dense) layer: Apply a fully connected layer with 1 neuron and a sigmoid activation.

def happyModel():
    """
    实现二进制分类模型的正向传播:
    ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE
    
    注意,为了简化和分级,您将对所有值进行硬编码

    例如步幅和内核(滤波器)大小。

    通常,函数应将这些值作为函数参数。
    
    Arguments:
    None

    Returns:
    model -- TF Keras model (包含整个培训过程信息的对象) 
    """
    model = tf.keras.Sequential([
            tf.keras.Input(shape=(64 , 64 ,3)),
            ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
            tfl.ZeroPadding2D(padding=3), # As import tensorflow.keras.layers as tfl
            ## Conv2D with 32 7x7 filters and stride of 1
            tfl.Conv2D(filters=32,kernel_size=7,strides=1),
            ## BatchNormalization for axis 3
            tfl.BatchNormalization(axis=3, momentum=0.99, epsilon=0.001),
            ## ReLU
            tfl.ReLU(),
            ## Max Pooling 2D with default parameters
            tfl.MaxPool2D(),
            ## Flatten layer
            tfl.Flatten(),
            ## Dense layer with 1 unit for output & 'sigmoid' activation
            tfl.Dense(1,activation='sigmoid')
        ])
    
    return model

我们来测试一下:

happy_model = happyModel()
# Print a summary for each layer
for layer in summary(happy_model):
    print(layer)
['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))]
['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform']
['BatchNormalization', (None, 64, 64, 32), 128]
['ReLU', (None, 64, 64, 32), 0]
['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid']
['Flatten', (None, 32768), 0]
['Dense', (None, 1), 32769, 'sigmoid']
现在您的模型已经创建,您可以编译它,以便使用优化器进行训练,而不必选择。当字符串精度指定为度量时,所使用的精度类型将根据所使用的损失函数自动转换。这是TensorFlow内置的众多优化之一,可以让您的生活更轻松!
好了,让我们去编译它吧!
happy_model.compile(optimizer='adam',
                   loss='binary_crossentropy',
                   metrics=['accuracy'])

是时候用.summary()方法检查模型的参数了。这将显示您拥有的图层类型、输出的形状以及每个图层中的参数数量。

happy_model.summary()
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 zero_padding2d (ZeroPadding  (None, 70, 70, 3)        0         
 2D)                                                             
                                                                 
 conv2d (Conv2D)             (None, 64, 64, 32)        4736      
                                                                 
 batch_normalization (BatchN  (None, 64, 64, 32)       128       
 ormalization)                                                   
                                                                 
 re_lu (ReLU)                (None, 64, 64, 32)        0         
                                                                 
 max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         
 )                                                               
                                                                 
 flatten (Flatten)           (None, 32768)             0         
                                                                 
 dense (Dense)               (None, 1)                 32769     
                                                                 
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64
_________________________________________________________________

 Train and Evaluate the Model

在创建了模型,使用您选择的优化器和损失函数对其进行编译,并对其内容进行了完整性检查之后,现在就可以开始构建了!

只需调用.fit()进行训练。就是这样!无需进行小批量、节省或复杂的反向传播计算。这一切都是为您完成的,因为您使用的是已经指定批次的TensorFlow数据集。如果您愿意,您可以选择指定纪元编号或小批量大小(例如,在未批处理数据集的情况下)。

happy_model.evaluate(X_test, Y_test)
5/5 [==============================] - 0s 10ms/step - loss: 0.1438 - accuracy: 0.9400

Out[28]:

[0.14378029108047485, 0.9399999976158142]

很简单,对吧?但是,如果您需要构建一个具有共享层、分支或多个输入和输出的模型呢?这正是Sequential凭借其简单而有限的功能无法帮助您的地方。

下一步:进入Functional API,这是一个稍微复杂、高度灵活的朋友。

 

《深度学习之卷积模型应用》

欢迎来到作业的下半部分,您将使用Keras灵活的Functional API构建一个ConvNet,它可以区分6个手语数字。

Functional API可以处理具有非线性拓扑、共享层以及具有多个输入或输出的层的模型。想象一下,当Sequential API要求模型以线性方式在其层中移动时,Functional API允许更大的灵活性。在Sequential是一条直线的情况下,Functional模型是一个图,其中层的节点可以以多种方式连接。

在下面的视觉示例中,顺序模型显示了一个可能的运动方向,与跳过连接形成对比,这只是构建功能模型的多种方式之一。您可能已经猜到,跳过连接会跳过网络中的某一层,并将输出馈送到网络中的稍后一层。别担心,你很快就会花更多时间在跳过连接上!

我们加载一下数据集

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_signs_dataset()

随机看一个数据

index = 4
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
y = 2

《深度学习之卷积模型应用》

 在之前的学习中,

您为该数据集构建了一个完全连接的网络。但由于这是一个图像数据集,因此将ConvNet应用于它更为自然。

首先,让我们检查数据的形状。

X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)

Forward Propagation

  • Conv2D:8个44的 filters, stride 1, 填充 is “SAME”
  • ReLU
  • MaxPool2D: 使用一个88 filter size and 一个(8,8)步长, padding is “SAME”
  • Conv2D: Use 16 2 by 2 filters, stride 1, padding is “SAME”
  • ReLU
  • MaxPool2D: Use a 4 by 4 filter size and a 4 by 4 stride, padding is “SAME”
  • Flatten the previous output.
  • Fully-connected (Dense) layer:应用具有6个神经元的完全连接层和softmax激活。

下面我们建立模型

def convolutional_model(input_shape):
    """
    Implements the forward propagation for the model:
    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE
    
    注意,为了简化和分级,您将硬编码一些值

    例如步幅和内核(滤波器)大小。

    通常,函数应将这些值作为函数参数。
    
    Arguments:
    input_img -- input dataset, of shape (input_shape)

    Returns:
    model -- TF Keras model (包含整个培训过程信息的对象) 
    """

    input_img = tf.keras.Input(shape=input_shape)
    ## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME'
    Z1 = tfl.Conv2D(filters= 8. , kernel_size=4 , padding='same',strides=1)(input_img)
    ## RELU
    A1 = tfl.ReLU()(Z1)
    ## MAXPOOL: window 8x8, stride 8, padding 'SAME'
    P1 = tfl.MaxPool2D(pool_size=8, strides=8, padding='SAME')(A1)
    ## CONV2D: 16 filters 2x2, stride 1, padding 'SAME'
    Z2 = tfl.Conv2D(filters= 16. , kernel_size=2 , padding='same',strides=1)(P1)
    ## RELU
    A2 =  tfl.ReLU()(Z2)
    ## MAXPOOL: window 4x4, stride 4, padding 'SAME'
    P2 = tfl.MaxPool2D(pool_size=4, strides=4, padding='SAME')(A2)
    ## FLATTEN
    F = tfl.Flatten()(P2)
    ## 全连接层
    ##输出层6个神经元。提示:其中一个参数应该是“activation='softmax'”
    outputs = tfl.Dense(units= 6 , activation='softmax')(F)
    model = tf.keras.Model(inputs=input_img, outputs=outputs)
    return model

编译并检查模型的参数

conv_model = convolutional_model((64, 64, 3))
conv_model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
conv_model.summary()
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_2 (InputLayer)        [(None, 64, 64, 3)]       0         
                                                                 
 conv2d_1 (Conv2D)           (None, 64, 64, 8)         392       
                                                                 
 re_lu_1 (ReLU)              (None, 64, 64, 8)         0         
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 8, 8, 8)          0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 8, 8, 16)          528       
                                                                 
 re_lu_2 (ReLU)              (None, 8, 8, 16)          0         
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 2, 2, 16)         0         
 2D)                                                             
                                                                 
 flatten_1 (Flatten)         (None, 64)                0         
                                                                 
 dense_1 (Dense)             (None, 6)                 390       
                                                                 
=================================================================
Total params: 1,310
Trainable params: 1,310
Non-trainable params: 0
_________________________________________________________________

Train the Model

train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)).batch(64)
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)).batch(64)
history = conv_model.fit(train_dataset, epochs=100, validation_data=test_dataset)
Epoch 1/100
17/17 [==============================] - 1s 52ms/step - loss: 1.7924 - accuracy: 0.1870 - val_loss: 1.7881 - val_accuracy: 0.1917
Epoch 2/100
17/17 [==============================] - 1s 64ms/step - loss: 1.7830 - accuracy: 0.2389 - val_loss: 1.7836 - val_accuracy: 0.2250
Epoch 3/100
17/17 [==============================] - 1s 69ms/step - loss: 1.7775 - accuracy: 0.2574 - val_loss: 1.7797 - val_accuracy: 0.1917
Epoch 4/100
17/17 [==============================] - 1s 60ms/step - loss: 1.7715 - accuracy: 0.2620 - val_loss: 1.7742 - val_accuracy: 0.2333
Epoch 5/100
17/17 [==============================] - 1s 54ms/step - loss: 1.7632 - accuracy: 0.3102 - val_loss: 1.7679 - val_accuracy: 0.2917
Epoch 6/100
17/17 [==============================] - 1s 59ms/step - loss: 1.7526 - accuracy: 0.3519 - val_loss: 1.7582 - val_accuracy: 0.3500
Epoch 7/100
17/17 [==============================] - 1s 63ms/step - loss: 1.7387 - accuracy: 0.3731 - val_loss: 1.7453 - val_accuracy: 0.3417
Epoch 8/100
17/17 [==============================] - 1s 62ms/step - loss: 1.7181 - accuracy: 0.3935 - val_loss: 1.7270 - val_accuracy: 0.3333
Epoch 9/100
17/17 [==============================] - 1s 55ms/step - loss: 1.6928 - accuracy: 0.4250 - val_loss: 1.7027 - val_accuracy: 0.3667
Epoch 10/100
17/17 [==============================] - 1s 51ms/step - loss: 1.6624 - accuracy: 0.4472 - val_loss: 1.6717 - val_accuracy: 0.3750
Epoch 11/100
17/17 [==============================] - 1s 44ms/step - loss: 1.6234 - accuracy: 0.4722 - val_loss: 1.6347 - val_accuracy: 0.4167
Epoch 12/100
17/17 [==============================] - 1s 41ms/step - loss: 1.5788 - accuracy: 0.4833 - val_loss: 1.5910 - val_accuracy: 0.4667
Epoch 13/100
17/17 [==============================] - 1s 49ms/step - loss: 1.5306 - accuracy: 0.5028 - val_loss: 1.5451 - val_accuracy: 0.5083
Epoch 14/100
17/17 [==============================] - 1s 50ms/step - loss: 1.4796 - accuracy: 0.5194 - val_loss: 1.4939 - val_accuracy: 0.5000
Epoch 15/100
17/17 [==============================] - 1s 48ms/step - loss: 1.4250 - accuracy: 0.5370 - val_loss: 1.4377 - val_accuracy: 0.5417
Epoch 16/100
17/17 [==============================] - 1s 43ms/step - loss: 1.3661 - accuracy: 0.5574 - val_loss: 1.3788 - val_accuracy: 0.5750
Epoch 17/100
17/17 [==============================] - 1s 49ms/step - loss: 1.3062 - accuracy: 0.5694 - val_loss: 1.3132 - val_accuracy: 0.6083
Epoch 18/100
17/17 [==============================] - 1s 43ms/step - loss: 1.2476 - accuracy: 0.5981 - val_loss: 1.2558 - val_accuracy: 0.5833
Epoch 19/100
17/17 [==============================] - 1s 44ms/step - loss: 1.1896 - accuracy: 0.6278 - val_loss: 1.2034 - val_accuracy: 0.6167
Epoch 20/100
17/17 [==============================] - 1s 45ms/step - loss: 1.1389 - accuracy: 0.6426 - val_loss: 1.1515 - val_accuracy: 0.6417
Epoch 21/100
17/17 [==============================] - 1s 44ms/step - loss: 1.0976 - accuracy: 0.6519 - val_loss: 1.1115 - val_accuracy: 0.6417
Epoch 22/100
17/17 [==============================] - 1s 49ms/step - loss: 1.0567 - accuracy: 0.6565 - val_loss: 1.0731 - val_accuracy: 0.6250
Epoch 23/100
17/17 [==============================] - 1s 48ms/step - loss: 1.0229 - accuracy: 0.6685 - val_loss: 1.0447 - val_accuracy: 0.6333
Epoch 24/100
17/17 [==============================] - 1s 49ms/step - loss: 0.9881 - accuracy: 0.6722 - val_loss: 1.0128 - val_accuracy: 0.6417
Epoch 25/100
17/17 [==============================] - 1s 53ms/step - loss: 0.9586 - accuracy: 0.6880 - val_loss: 0.9859 - val_accuracy: 0.6500
Epoch 26/100
17/17 [==============================] - 1s 42ms/step - loss: 0.9345 - accuracy: 0.6954 - val_loss: 0.9655 - val_accuracy: 0.6500
Epoch 27/100
17/17 [==============================] - 1s 56ms/step - loss: 0.9080 - accuracy: 0.7009 - val_loss: 0.9405 - val_accuracy: 0.6583
Epoch 28/100
17/17 [==============================] - 1s 44ms/step - loss: 0.8859 - accuracy: 0.7120 - val_loss: 0.9210 - val_accuracy: 0.6667
Epoch 29/100
17/17 [==============================] - 1s 46ms/step - loss: 0.8638 - accuracy: 0.7213 - val_loss: 0.8993 - val_accuracy: 0.6667
Epoch 30/100
17/17 [==============================] - 1s 44ms/step - loss: 0.8460 - accuracy: 0.7324 - val_loss: 0.8815 - val_accuracy: 0.6667
Epoch 31/100
17/17 [==============================] - 1s 45ms/step - loss: 0.8278 - accuracy: 0.7389 - val_loss: 0.8654 - val_accuracy: 0.6667
Epoch 32/100
17/17 [==============================] - 1s 65ms/step - loss: 0.8084 - accuracy: 0.7426 - val_loss: 0.8504 - val_accuracy: 0.6750
Epoch 33/100
17/17 [==============================] - 1s 47ms/step - loss: 0.7896 - accuracy: 0.7509 - val_loss: 0.8345 - val_accuracy: 0.6833
Epoch 34/100
17/17 [==============================] - 1s 63ms/step - loss: 0.7741 - accuracy: 0.7537 - val_loss: 0.8211 - val_accuracy: 0.7000
Epoch 35/100
17/17 [==============================] - 1s 48ms/step - loss: 0.7585 - accuracy: 0.7565 - val_loss: 0.8074 - val_accuracy: 0.7083
Epoch 36/100
17/17 [==============================] - 1s 48ms/step - loss: 0.7439 - accuracy: 0.7639 - val_loss: 0.7955 - val_accuracy: 0.7083
Epoch 37/100
17/17 [==============================] - 1s 52ms/step - loss: 0.7297 - accuracy: 0.7694 - val_loss: 0.7830 - val_accuracy: 0.7083
Epoch 38/100
17/17 [==============================] - 1s 48ms/step - loss: 0.7170 - accuracy: 0.7741 - val_loss: 0.7712 - val_accuracy: 0.7250
Epoch 39/100
17/17 [==============================] - 1s 47ms/step - loss: 0.7036 - accuracy: 0.7731 - val_loss: 0.7596 - val_accuracy: 0.7333
Epoch 40/100
17/17 [==============================] - 1s 44ms/step - loss: 0.6921 - accuracy: 0.7824 - val_loss: 0.7491 - val_accuracy: 0.7417
Epoch 41/100
17/17 [==============================] - 1s 64ms/step - loss: 0.6797 - accuracy: 0.7843 - val_loss: 0.7382 - val_accuracy: 0.7583
Epoch 42/100
17/17 [==============================] - 1s 48ms/step - loss: 0.6682 - accuracy: 0.7917 - val_loss: 0.7290 - val_accuracy: 0.7667
Epoch 43/100
17/17 [==============================] - 1s 66ms/step - loss: 0.6567 - accuracy: 0.7963 - val_loss: 0.7184 - val_accuracy: 0.7750
Epoch 44/100
17/17 [==============================] - 1s 42ms/step - loss: 0.6469 - accuracy: 0.7991 - val_loss: 0.7106 - val_accuracy: 0.7750
Epoch 45/100
17/17 [==============================] - 1s 45ms/step - loss: 0.6362 - accuracy: 0.8009 - val_loss: 0.7016 - val_accuracy: 0.7750
Epoch 46/100
17/17 [==============================] - 1s 57ms/step - loss: 0.6263 - accuracy: 0.8019 - val_loss: 0.6939 - val_accuracy: 0.7750
Epoch 47/100
17/17 [==============================] - 1s 45ms/step - loss: 0.6172 - accuracy: 0.8065 - val_loss: 0.6859 - val_accuracy: 0.7833
Epoch 48/100
17/17 [==============================] - 1s 49ms/step - loss: 0.6076 - accuracy: 0.8083 - val_loss: 0.6784 - val_accuracy: 0.7917
Epoch 49/100
17/17 [==============================] - 1s 47ms/step - loss: 0.5992 - accuracy: 0.8102 - val_loss: 0.6711 - val_accuracy: 0.7917
Epoch 50/100
17/17 [==============================] - 1s 62ms/step - loss: 0.5904 - accuracy: 0.8093 - val_loss: 0.6638 - val_accuracy: 0.8000
Epoch 51/100
17/17 [==============================] - 1s 48ms/step - loss: 0.5822 - accuracy: 0.8120 - val_loss: 0.6571 - val_accuracy: 0.8000
Epoch 52/100
17/17 [==============================] - 1s 56ms/step - loss: 0.5736 - accuracy: 0.8157 - val_loss: 0.6508 - val_accuracy: 0.8083
Epoch 53/100
17/17 [==============================] - 1s 53ms/step - loss: 0.5664 - accuracy: 0.8204 - val_loss: 0.6448 - val_accuracy: 0.8083
Epoch 54/100
17/17 [==============================] - 1s 56ms/step - loss: 0.5586 - accuracy: 0.8194 - val_loss: 0.6385 - val_accuracy: 0.8083
Epoch 55/100
17/17 [==============================] - 1s 58ms/step - loss: 0.5509 - accuracy: 0.8204 - val_loss: 0.6341 - val_accuracy: 0.8083
Epoch 56/100
17/17 [==============================] - 1s 51ms/step - loss: 0.5444 - accuracy: 0.8222 - val_loss: 0.6277 - val_accuracy: 0.8000
Epoch 57/100
17/17 [==============================] - 1s 62ms/step - loss: 0.5386 - accuracy: 0.8222 - val_loss: 0.6241 - val_accuracy: 0.8000
Epoch 58/100

17/17 [==============================] - 1s 48ms/step - loss: 0.5296 - accuracy: 0.8296 - val_loss: 0.6193 - val_accuracy: 0.8167
Epoch 59/100
17/17 [==============================] - 1s 63ms/step - loss: 0.5240 - accuracy: 0.8296 - val_loss: 0.6141 - val_accuracy: 0.8000
Epoch 60/100
17/17 [==============================] - 1s 45ms/step - loss: 0.5175 - accuracy: 0.8333 - val_loss: 0.6084 - val_accuracy: 0.8083
Epoch 61/100
17/17 [==============================] - 1s 53ms/step - loss: 0.5093 - accuracy: 0.8352 - val_loss: 0.6047 - val_accuracy: 0.8250
Epoch 62/100
17/17 [==============================] - 1s 50ms/step - loss: 0.5044 - accuracy: 0.8370 - val_loss: 0.5991 - val_accuracy: 0.8083
Epoch 63/100
17/17 [==============================] - 1s 53ms/step - loss: 0.4981 - accuracy: 0.8333 - val_loss: 0.5955 - val_accuracy: 0.8167
Epoch 64/100
17/17 [==============================] - 1s 57ms/step - loss: 0.4902 - accuracy: 0.8380 - val_loss: 0.5926 - val_accuracy: 0.8250
Epoch 65/100
17/17 [==============================] - 1s 48ms/step - loss: 0.4853 - accuracy: 0.8444 - val_loss: 0.5882 - val_accuracy: 0.8083
Epoch 66/100
17/17 [==============================] - 1s 48ms/step - loss: 0.4794 - accuracy: 0.8472 - val_loss: 0.5839 - val_accuracy: 0.8083
Epoch 67/100
17/17 [==============================] - 1s 44ms/step - loss: 0.4724 - accuracy: 0.8519 - val_loss: 0.5809 - val_accuracy: 0.8167
Epoch 68/100
17/17 [==============================] - 1s 54ms/step - loss: 0.4680 - accuracy: 0.8528 - val_loss: 0.5760 - val_accuracy: 0.8083
Epoch 69/100
17/17 [==============================] - 1s 46ms/step - loss: 0.4623 - accuracy: 0.8546 - val_loss: 0.5719 - val_accuracy: 0.8250
Epoch 70/100
17/17 [==============================] - 1s 49ms/step - loss: 0.4559 - accuracy: 0.8574 - val_loss: 0.5692 - val_accuracy: 0.8083
Epoch 71/100
17/17 [==============================] - 1s 57ms/step - loss: 0.4518 - accuracy: 0.8593 - val_loss: 0.5650 - val_accuracy: 0.8083
Epoch 72/100
17/17 [==============================] - 1s 60ms/step - loss: 0.4452 - accuracy: 0.8620 - val_loss: 0.5624 - val_accuracy: 0.8250
Epoch 73/100
17/17 [==============================] - 1s 52ms/step - loss: 0.4415 - accuracy: 0.8639 - val_loss: 0.5590 - val_accuracy: 0.8167
Epoch 74/100
17/17 [==============================] - 1s 55ms/step - loss: 0.4361 - accuracy: 0.8648 - val_loss: 0.5554 - val_accuracy: 0.8250
Epoch 75/100
17/17 [==============================] - 1s 58ms/step - loss: 0.4298 - accuracy: 0.8704 - val_loss: 0.5528 - val_accuracy: 0.8333
Epoch 76/100
17/17 [==============================] - 1s 62ms/step - loss: 0.4262 - accuracy: 0.8685 - val_loss: 0.5490 - val_accuracy: 0.8250
Epoch 77/100
17/17 [==============================] - 1s 48ms/step - loss: 0.4215 - accuracy: 0.8713 - val_loss: 0.5460 - val_accuracy: 0.8250
Epoch 78/100
17/17 [==============================] - 1s 46ms/step - loss: 0.4151 - accuracy: 0.8787 - val_loss: 0.5436 - val_accuracy: 0.8250
Epoch 79/100
17/17 [==============================] - 1s 43ms/step - loss: 0.4113 - accuracy: 0.8787 - val_loss: 0.5407 - val_accuracy: 0.8167
Epoch 80/100
17/17 [==============================] - 1s 43ms/step - loss: 0.4062 - accuracy: 0.8806 - val_loss: 0.5384 - val_accuracy: 0.8167
Epoch 81/100
17/17 [==============================] - 1s 48ms/step - loss: 0.4020 - accuracy: 0.8806 - val_loss: 0.5348 - val_accuracy: 0.8167
Epoch 82/100
17/17 [==============================] - 1s 50ms/step - loss: 0.3962 - accuracy: 0.8824 - val_loss: 0.5323 - val_accuracy: 0.8167
Epoch 83/100
17/17 [==============================] - 1s 45ms/step - loss: 0.3927 - accuracy: 0.8824 - val_loss: 0.5297 - val_accuracy: 0.8250
Epoch 84/100
17/17 [==============================] - 1s 51ms/step - loss: 0.3881 - accuracy: 0.8843 - val_loss: 0.5272 - val_accuracy: 0.8250
Epoch 85/100
17/17 [==============================] - 1s 46ms/step - loss: 0.3832 - accuracy: 0.8870 - val_loss: 0.5249 - val_accuracy: 0.8250
Epoch 86/100
17/17 [==============================] - 1s 53ms/step - loss: 0.3796 - accuracy: 0.8898 - val_loss: 0.5215 - val_accuracy: 0.8250
Epoch 87/100
17/17 [==============================] - 1s 56ms/step - loss: 0.3743 - accuracy: 0.8889 - val_loss: 0.5196 - val_accuracy: 0.8250
Epoch 88/100
17/17 [==============================] - 1s 48ms/step - loss: 0.3710 - accuracy: 0.8907 - val_loss: 0.5164 - val_accuracy: 0.8250
Epoch 89/100
17/17 [==============================] - 1s 45ms/step - loss: 0.3660 - accuracy: 0.8917 - val_loss: 0.5139 - val_accuracy: 0.8333
Epoch 90/100
17/17 [==============================] - 1s 45ms/step - loss: 0.3626 - accuracy: 0.8917 - val_loss: 0.5106 - val_accuracy: 0.8333
Epoch 91/100
17/17 [==============================] - 1s 48ms/step - loss: 0.3579 - accuracy: 0.8944 - val_loss: 0.5090 - val_accuracy: 0.8500
Epoch 92/100
17/17 [==============================] - 1s 49ms/step - loss: 0.3547 - accuracy: 0.8935 - val_loss: 0.5060 - val_accuracy: 0.8417
Epoch 93/100
17/17 [==============================] - 1s 44ms/step - loss: 0.3501 - accuracy: 0.8944 - val_loss: 0.5038 - val_accuracy: 0.8500
Epoch 94/100
17/17 [==============================] - 1s 47ms/step - loss: 0.3468 - accuracy: 0.8954 - val_loss: 0.5014 - val_accuracy: 0.8417
Epoch 95/100
17/17 [==============================] - 1s 43ms/step - loss: 0.3424 - accuracy: 0.8954 - val_loss: 0.4996 - val_accuracy: 0.8500
Epoch 96/100
17/17 [==============================] - 1s 64ms/step - loss: 0.3395 - accuracy: 0.8963 - val_loss: 0.4970 - val_accuracy: 0.8417
Epoch 97/100
17/17 [==============================] - 1s 47ms/step - loss: 0.3351 - accuracy: 0.9000 - val_loss: 0.4950 - val_accuracy: 0.8417
Epoch 98/100
17/17 [==============================] - 1s 54ms/step - loss: 0.3323 - accuracy: 0.8981 - val_loss: 0.4933 - val_accuracy: 0.8333
Epoch 99/100
17/17 [==============================] - 1s 48ms/step - loss: 0.3280 - accuracy: 0.9000 - val_loss: 0.4916 - val_accuracy: 0.8417
Epoch 100/100
17/17 [==============================] - 1s 57ms/step - loss: 0.3251 - accuracy: 0.9028 - val_loss: 0.4894 - val_accuracy: 0.8333

history对象是.fit()操作的输出,并提供内存中所有损失和度量值的记录。它存储为字典,您可以在history中检索。history:

history.history
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 'accuracy': [0.18703703582286835,
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 'val_loss': [1.7880679368972778,
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  0.8416666388511658,
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现在,使用history.history可视化时间损失:
df_loss_acc = pd.DataFrame(history.history)
df_loss= df_loss_acc[['loss','val_loss']]
df_loss.rename(columns={'loss':'train','val_loss':'validation'},inplace=True)
df_acc= df_loss_acc[['accuracy','val_accuracy']]
df_acc.rename(columns={'accuracy':'train','val_accuracy':'validation'},inplace=True)
df_loss.plot(title='Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss')
df_acc.plot(title='Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy')
plt.show()

《深度学习之卷积模型应用》

 

 《深度学习之卷积模型应用》

 

 

    原文作者:故y
    原文地址: https://www.cnblogs.com/kk-style/p/16905768.html
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
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