TensorFlow识别CiFar10物品分类

 

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.preprocessing import StandardScaler

 

In [2]:

(x_train_all, y_train_all), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train_all.shape

 

Out[2]:

(50000, 32, 32, 3)

 

In [3]:

x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform( x_train.astype(np.float32).reshape(-1,1) ).reshape(-1, 32, 32, 3)
x_valid_scaled = scaler.transform( x_valid.astype(np.float32).reshape(-1,1) ).reshape(-1, 32, 32, 3)
x_test_scaled = scaler.transform( x_test.astype(np.float32).reshape(-1,1) ).reshape(-1, 32, 32, 3)

 

In [4]:

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=[32, 32, 3]))
model.add(tf.keras.layers.Dense(512, activation='selu'))
model.add(tf.keras.layers.AlphaDropout(0.2))
model.add(tf.keras.layers.Dense(256, activation='selu'))
model.add(tf.keras.layers.Dense(128, activation='selu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# 配置网络
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])
callbacks = [
    tf.keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)
]
# 训练
history = model.fit(x_train_scaled, y_train, epochs=10, validation_data=(x_valid_scaled, y_valid), callbacks=callbacks)

 

 

Epoch 1/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.9129 - acc: 0.3619 - val_loss: 1.6277 - val_acc: 0.4324
Epoch 2/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.5629 - acc: 0.4414 - val_loss: 1.5839 - val_acc: 0.4422
Epoch 3/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.4790 - acc: 0.4725 - val_loss: 1.5413 - val_acc: 0.4626
Epoch 4/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.4196 - acc: 0.4940 - val_loss: 1.4940 - val_acc: 0.4888
Epoch 5/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.3624 - acc: 0.5118 - val_loss: 1.4644 - val_acc: 0.4958
Epoch 6/10
1407/1407 [==============================] - 13s 9ms/step - loss: 1.3151 - acc: 0.5298 - val_loss: 1.5182 - val_acc: 0.4866
Epoch 7/10
1407/1407 [==============================] - 14s 10ms/step - loss: 1.2662 - acc: 0.5484 - val_loss: 1.5000 - val_acc: 0.5164
Epoch 8/10
1407/1407 [==============================] - 14s 10ms/step - loss: 1.2238 - acc: 0.5640 - val_loss: 1.5809 - val_acc: 0.4986
Epoch 9/10
1407/1407 [==============================] - 14s 10ms/step - loss: 1.1800 - acc: 0.5755 - val_loss: 1.5581 - val_acc: 0.5086
Epoch 10/10
1407/1407 [==============================] - 14s 10ms/step - loss: 1.1452 - acc: 0.5930 - val_loss: 1.4877 - val_acc: 0.5300

 

In [5]:

pd.DataFrame(history.history)

	loss	acc	val_loss	val_acc
0	1.912864	0.361933	1.627748	0.4324
1	1.562863	0.441444	1.583859	0.4422
2	1.479033	0.472489	1.541267	0.4626
3	1.419567	0.494044	1.494043	0.4888
4	1.362388	0.511800	1.464406	0.4958
5	1.315090	0.529844	1.518175	0.4866
6	1.266191	0.548422	1.499979	0.5164
7	1.223794	0.564000	1.580877	0.4986
8	1.179963	0.575489	1.558058	0.5086
9	1.145220	0.593000	1.487734	0.5300

 

In [10]:

pd.DataFrame(history.history).plot(figsize=(8,4))
plt.grid()
plt.gca().set_ylim(0,2)
plt.show()

 

 

 

In [7]:

model.evaluate(x_test_scaled, y_test)

 

 

313/313 [==============================] - 1s 2ms/step - loss: 1.4554 - acc: 0.5235

Out[7]:

[1.4553571939468384, 0.5235000252723694]

 

In [8]:

model.summary()

 

 

Model: "sequential"
_________________________________________________________________
 Layer (type)                  Output Shape              Param #   
=================================================================
 flatten (Flatten)             (None, 3072)              0                                                               
 dense (Dense)                 (None, 512)               1573376                                                        
 alpha_dropout (AlphaDropout)  (None, 512)               0                                                                                                                           
 dense_1 (Dense)               (None, 256)               131328                                                      
 dense_2 (Dense)               (None, 128)               32896                                                     
 dense_3 (Dense)               (None, 10)                1290                                                         
=================================================================
Total params: 1738890 (6.63 MB)
Trainable params: 1738890 (6.63 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________