基於tensorflow_gpu 1.9.0實現的第二個神經網路：對影評的二值分類

阿新 • • 發佈：2019-01-15

示例來源於Tensorflow的官方教程。
基於tensorflow_gpu 1.9.0實現的第二個神經網路：對影評的二值分類，程式碼如下：

#!/usr/bin/env python
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

# Download the IMDB dataset
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data,
                             test_labels) = imdb.load_data(num_words=10000 
)

# Explore the data
print("Training entries: {}, labels: {}".format(
    len(train_data), len(train_labels)))
print(train_data[0])
len(train_data[0]), len(train_data[1])

# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()

# The first indices are reserved
word_index = {k: (v+3 
) for k, v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["UNUSED"] = 3

reverse_word_index = dict([(value, key)
                           for (key, value) in word_index.items()])


def decode_review(text):
    return 
 ' '.join([reverse_word_index.get(i, '?') for i in text])


decode_review(train_data[0])

# Prepare the data
# The reviews--the arrays of integers--must be converted to tensors before fed
# into the neural network.
# Since the movie reviews must be the same length, we will use the pad_sequences
# function to standardize the lengths.
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                        value=word_index["<PAD>"],
                                                        padding='post',
                                                        maxlen=256)

test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                       value=word_index["<PAD>"],
                                                       padding='post',
                                                       maxlen=256)
len(train_data[0]), len(train_data[1])
print(train_data[0])

# Build the model

# Input shape is the vocabulary count used for the movie reviews (10, 000 words)
vocab_size = 10000

model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

model.summary()

# Loss function and optimizer
model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Create a validation set
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]

# Train the model
history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=40,
                    batch_size=512,
                    validation_data=(x_val, y_val),
                    verbose=1)

# Evaluate the model
results = model.evaluate(test_data, test_labels)
print(results)

# Create a graph of accuracy and loss over time
history_dict = history.history
history_dict.keys()
acc = history_dict['acc']
val_acc = history_dict['val_acc']
loss = history_dict['loss']
val_loss = history_dict['val_loss']

epochs = range(1, len(acc) + 1)

plt.figure()
# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# "b" is for "solid blue line"
plt.plot(epochs, val_loss, 'b', label='Validattion loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.figure()
# "bo" is for "blue dot"
plt.plot(epochs, acc, 'bo', label='Training accuracy')
# "b" is for "solid blue line"
plt.plot(epochs, val_acc, 'b', label='Validattion accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()

執行該程式之前，需要先根據我的另一篇部落格《Ubuntu 16.04安裝tensorflow_gpu 1.9.0的方法》，安裝tensorflow_gpu 1.9.0，然後安裝numpy、matplotlib等工具包，此外還需要安裝python-tk，命令如下：

sudo pip install numpy
sudo pip install matplotlib
sudo apt-get install python-tk

執行結果如下：

1.9.0
Training entries: 25000, labels: 25000
[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]
[   1   14   22   16   43  530  973 1622 1385   65  458 4468   66 3941
    4  173   36  256    5   25  100   43  838  112   50  670    2    9
   35  480  284    5  150    4  172  112  167    2  336  385   39    4
  172 4536 1111   17  546   38   13  447    4  192   50   16    6  147
 2025   19   14   22    4 1920 4613  469    4   22   71   87   12   16
   43  530   38   76   15   13 1247    4   22   17  515   17   12   16
  626   18    2    5   62  386   12    8  316    8  106    5    4 2223
 5244   16  480   66 3785   33    4  130   12   16   38  619    5   25
  124   51   36  135   48   25 1415   33    6   22   12  215   28   77
   52    5   14  407   16   82    2    8    4  107  117 5952   15  256
    4    2    7 3766    5  723   36   71   43  530  476   26  400  317
   46    7    4    2 1029   13  104   88    4  381   15  297   98   32
 2071   56   26  141    6  194 7486   18    4  226   22   21  134  476
   26  480    5  144   30 5535   18   51   36   28  224   92   25  104
    4  226   65   16   38 1334   88   12   16  283    5   16 4472  113
  103   32   15   16 5345   19  178   32    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0]
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, None, 16)          160000    
_________________________________________________________________
global_average_pooling1d (Gl (None, 16)                0         
_________________________________________________________________
dense (Dense)                (None, 16)                272       
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 17        
=================================================================
Total params: 160,289
Trainable params: 160,289
Non-trainable params: 0
_________________________________________________________________
Train on 15000 samples, validate on 10000 samples
Epoch 1/40
2018-07-26 16:23:47.699236: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-26 16:23:47.754868: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-07-26 16:23:47.755179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties: 
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 2.92GiB
2018-07-26 16:23:47.755192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0
2018-07-26 16:23:47.927017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-26 16:23:47.927050: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958]      0 
2018-07-26 16:23:47.927060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   N 
2018-07-26 16:23:47.927246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2635 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
15000/15000 [==============================] - 1s 57us/step - loss: 0.6918 - acc: 0.5469 - val_loss: 0.6910 - val_acc: 0.5332
Epoch 2/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.6896 - acc: 0.5797 - val_loss: 0.6885 - val_acc: 0.6327
Epoch 3/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.6869 - acc: 0.6238 - val_loss: 0.6859 - val_acc: 0.6377
Epoch 4/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.6829 - acc: 0.6443 - val_loss: 0.6821 - val_acc: 0.5562
Epoch 5/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.6767 - acc: 0.7030 - val_loss: 0.6741 - val_acc: 0.7263
Epoch 6/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.6678 - acc: 0.7368 - val_loss: 0.6643 - val_acc: 0.7128
Epoch 7/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.6551 - acc: 0.7505 - val_loss: 0.6508 - val_acc: 0.7452
Epoch 8/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.6381 - acc: 0.7613 - val_loss: 0.6337 - val_acc: 0.7558
Epoch 9/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.6176 - acc: 0.7673 - val_loss: 0.6128 - val_acc: 0.7574
Epoch 10/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.5929 - acc: 0.7845 - val_loss: 0.5891 - val_acc: 0.7712
Epoch 11/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.5659 - acc: 0.7891 - val_loss: 0.5666 - val_acc: 0.7760
Epoch 12/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.5382 - acc: 0.8040 - val_loss: 0.5381 - val_acc: 0.7920
Epoch 13/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.5087 - acc: 0.8211 - val_loss: 0.5133 - val_acc: 0.8060
Epoch 14/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.4810 - acc: 0.8326 - val_loss: 0.4889 - val_acc: 0.8153
Epoch 15/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.4544 - acc: 0.8431 - val_loss: 0.4655 - val_acc: 0.8200
Epoch 16/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.4299 - acc: 0.8486 - val_loss: 0.4442 - val_acc: 0.8329
Epoch 17/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.4061 - acc: 0.8603 - val_loss: 0.4252 - val_acc: 0.8394
Epoch 18/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.3851 - acc: 0.8675 - val_loss: 0.4080 - val_acc: 0.8444
Epoch 19/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.3664 - acc: 0.8734 - val_loss: 0.3931 - val_acc: 0.8474
Epoch 20/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.3494 - acc: 0.8794 - val_loss: 0.3799 - val_acc: 0.8534
Epoch 21/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.3341 - acc: 0.8845 - val_loss: 0.3684 - val_acc: 0.8582
Epoch 22/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.3203 - acc: 0.8880 - val_loss: 0.3584 - val_acc: 0.8624
Epoch 23/40
15000/15000 [==============================] - 0s 18us/step - loss: 0.3080 - acc: 0.8917 - val_loss: 0.3498 - val_acc: 0.8647
Epoch 24/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2963 - acc: 0.8957 - val_loss: 0.3418 - val_acc: 0.8682
Epoch 25/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2859 - acc: 0.9005 - val_loss: 0.3349 - val_acc: 0.8688
Epoch 26/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2762 - acc: 0.9036 - val_loss: 0.3292 - val_acc: 0.8728
Epoch 27/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2676 - acc: 0.9045 - val_loss: 0.3238 - val_acc: 0.8722
Epoch 28/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2588 - acc: 0.9087 - val_loss: 0.3185 - val_acc: 0.8740
Epoch 29/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2512 - acc: 0.9119 - val_loss: 0.3147 - val_acc: 0.8757
Epoch 30/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2447 - acc: 0.9129 - val_loss: 0.3106 - val_acc: 0.8755
Epoch 31/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2369 - acc: 0.9167 - val_loss: 0.3075 - val_acc: 0.8777
Epoch 32/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2310 - acc: 0.9175 - val_loss: 0.3045 - val_acc: 0.8779
Epoch 33/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2241 - acc: 0.9216 - val_loss: 0.3018 - val_acc: 0.8793
Epoch 34/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.2184 - acc: 0.9229 - val_loss: 0.2999 - val_acc: 0.8786
Epoch 35/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.2133 - acc: 0.9237 - val_loss: 0.2973 - val_acc: 0.8820
Epoch 36/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.2073 - acc: 0.9269 - val_loss: 0.2956 - val_acc: 0.8808
Epoch 37/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.2022 - acc: 0.9286 - val_loss: 0.2940 - val_acc: 0.8804
Epoch 38/40
15000/15000 [==============================] - 0s 20us/step - loss: 0.1974 - acc: 0.9303 - val_loss: 0.2924 - val_acc: 0.8811
Epoch 39/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.1923 - acc: 0.9327 - val_loss: 0.2910 - val_acc: 0.8837
Epoch 40/40
15000/15000 [==============================] - 0s 19us/step - loss: 0.1878 - acc: 0.9338 - val_loss: 0.2898 - val_acc: 0.8836
25000/25000 [==============================] - 0s 20us/step
[0.3051770512676239, 0.87472]

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