tensorflow入门（3）运行一段代码

运行一段博客上的代码：

# encoding: utf-8import tensorflow as tf  # 0.12import numpy as npimport osfrom collections import Counterimport librosa  # https://github.com/librosa/librosawav_path = '/home/liuyuan/Desktop/data/wav/train'label_file = '/home/liuyuan/Desktop/data/doc/trans/train.word.txt'def get_wav_files(wav_path=wav_path):    wav_files = []    for (dirpath, dirnames, filenames) in os.walk(wav_path):        for filename in filenames:            if filename.endswith('.wav') or filename.endswith('.WAV'):                filename_path = os.sep.join([dirpath, filename])                if os.stat(filename_path).st_size < 240000:                    continue                wav_files.append(filename_path)    return wav_fileswav_files = get_wav_files()def get_wav_lable(wav_files=wav_files, label_file=label_file):    labels_dict = {}    with open(label_file, 'r') as f:        for label in f:            label = label.strip('\n')            label_id = label.split(' ', 1)[0]            label_text = label.split(' ', 1)[1]            labels_dict[label_id] = label_text    labels = []    new_wav_files = []    for wav_file in wav_files:        wav_id = os.path.basename(wav_file).split('.')[0]        if wav_id in labels_dict:            labels.append(labels_dict[wav_id])            new_wav_files.append(wav_file)    return new_wav_files, labelswav_files, labels = get_wav_lable()print("样本数:", len(wav_files))  # 8911# print(wav_files[0], labels[0])# wav/train/A11/A11_0.WAV -> 绿 是 阳春 烟 景 大块 文章 的 底色 四月 的 林 峦 更是 绿 得 鲜活 秀媚 诗意 盎然# 词汇表(参看练习1和7)all_words = []for label in labels:    all_words += [word for word in label]counter = Counter(all_words)count_pairs = sorted(counter.items(), key=lambda x: -x[1])words, _ = zip(*count_pairs)words_size = len(words)print('词汇表大小:', words_size)word_num_map = dict(zip(words, range(len(words))))to_num = lambda word: word_num_map.get(word, len(words))labels_vector = [list(map(to_num, label)) for label in labels]# print(wavs_file[0], labels_vector[0])# wav/train/A11/A11_0.WAV -> [479, 0, 7, 0, 138, 268, 0, 222, 0, 714, 0, 23, 261, 0, 28, 1191, 0, 1, 0, 442, 199, 0, 72, 38, 0, 1, 0, 463, 0, 1184, 0, 269, 7, 0, 479, 0, 70, 0, 816, 254, 0, 675, 1707, 0, 1255, 136, 0, 2020, 91]# print(words[479]) #绿label_max_len = np.max([len(label) for label in labels_vector])print('最长句子的字数:', label_max_len)wav_max_len = 0  # 673for wav in wav_files:    wav, sr = librosa.load(wav, mono=True)    mfcc = np.transpose(librosa.feature.mfcc(wav, sr), [1, 0])    if len(mfcc) > wav_max_len:        wav_max_len = len(mfcc)print("最长的语音:", wav_max_len)batch_size = 16n_batch = len(wav_files) // batch_size# 获得一个batchpointer = 0def get_next_batches(batch_size):    global pointer    batches_wavs = []    batches_labels = []    for i in range(batch_size):        wav, sr = librosa.load(wav_files[pointer], mono=True)        mfcc = np.transpose(librosa.feature.mfcc(wav, sr), [1, 0])        batches_wavs.append(mfcc.tolist())        batches_labels.append(labels_vector[pointer])        pointer += 1        # 补零对齐    for mfcc in batches_wavs:        while len(mfcc) < wav_max_len:            mfcc.append([0] * 20)    for label in batches_labels:        while len(label) < label_max_len:            label.append(0)    return batches_wavs, batches_labelsX = tf.placeholder(dtype=tf.float32, shape=[batch_size, None, 20])sequence_len = tf.reduce_sum(tf.cast(tf.not_equal(tf.reduce_sum(X, reduction_indices=2), 0.), tf.int32),                             reduction_indices=1)Y = tf.placeholder(dtype=tf.int32, shape=[batch_size, None])# conv1d_layerconv1d_index = 0def conv1d_layer(input_tensor, size, dim, activation, scale, bias):    global conv1d_index    with tf.variable_scope('conv1d_' + str(conv1d_index)):        W = tf.get_variable('W', (size, input_tensor.get_shape().as_list()[-1], dim), dtype=tf.float32,                            initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale))        if bias:            b = tf.get_variable('b', [dim], dtype=tf.float32, initializer=tf.constant_initializer(0))        out = tf.nn.conv1d(input_tensor, W, stride=1, padding='SAME') + (b if bias else 0)        if not bias:            beta = tf.get_variable('beta', dim, dtype=tf.float32, initializer=tf.constant_initializer(0))            gamma = tf.get_variable('gamma', dim, dtype=tf.float32, initializer=tf.constant_initializer(1))            mean_running = tf.get_variable('mean', dim, dtype=tf.float32, initializer=tf.constant_initializer(0))            variance_running = tf.get_variable('variance', dim, dtype=tf.float32,                                               initializer=tf.constant_initializer(1))            mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1))            def update_running_stat():                decay = 0.99                update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)),                             variance_running.assign(variance_running * decay + variance * (1 - decay))]                with tf.control_dependencies(update_op):                    return tf.identity(mean), tf.identity(variance)                m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]),                               update_running_stat, lambda: (mean_running, variance_running))                out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8)        if activation == 'tanh':            out = tf.nn.tanh(out)        if activation == 'sigmoid':            out = tf.nn.sigmoid(out)        conv1d_index += 1        return out        # aconv1d_layeraconv1d_index = 0def aconv1d_layer(input_tensor, size, rate, activation, scale, bias):    global aconv1d_index    with tf.variable_scope('aconv1d_' + str(aconv1d_index)):        shape = input_tensor.get_shape().as_list()        W = tf.get_variable('W', (1, size, shape[-1], shape[-1]), dtype=tf.float32,                            initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale))        if bias:            b = tf.get_variable('b', [shape[-1]], dtype=tf.float32, initializer=tf.constant_initializer(0))        out = tf.nn.atrous_conv2d(tf.expand_dims(input_tensor, dim=1), W, rate=rate, padding='SAME')        out = tf.squeeze(out, [1])        if not bias:            beta = tf.get_variable('beta', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0))            gamma = tf.get_variable('gamma', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(1))            mean_running = tf.get_variable('mean', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0))            variance_running = tf.get_variable('variance', shape[-1], dtype=tf.float32,                                               initializer=tf.constant_initializer(1))            mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1))            def update_running_stat():                decay = 0.99                update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)),                             variance_running.assign(variance_running * decay + variance * (1 - decay))]                with tf.control_dependencies(update_op):                    return tf.identity(mean), tf.identity(variance)                m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]),                               update_running_stat, lambda: (mean_running, variance_running))                out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8)        if activation == 'tanh':            out = tf.nn.tanh(out)        if activation == 'sigmoid':            out = tf.nn.sigmoid(out)        aconv1d_index += 1        return out        # 定义神经网络def speech_to_text_network(n_dim=128, n_blocks=3):    out = conv1d_layer(input_tensor=X, size=1, dim=n_dim, activation='tanh', scale=0.14, bias=False)    # skip connections    def residual_block(input_sensor, size, rate):        conv_filter = aconv1d_layer(input_sensor, size=size, rate=rate, activation='tanh', scale=0.03, bias=False)        conv_gate = aconv1d_layer(input_sensor, size=size, rate=rate, activation='sigmoid', scale=0.03, bias=False)        out = conv_filter * conv_gate        out = conv1d_layer(out, size=1, dim=n_dim, activation='tanh', scale=0.08, bias=False)        return out + input_sensor, out    skip = 0    for _ in range(n_blocks):        for r in [1, 2, 4, 8, 16]:            out, s = residual_block(out, size=7, rate=r)            skip += s    logit = conv1d_layer(skip, size=1, dim=skip.get_shape().as_list()[-1], activation='tanh', scale=0.08, bias=False)    logit = conv1d_layer(logit, size=1, dim=words_size, activation=None, scale=0.04, bias=True)    return logitclass MaxPropOptimizer(tf.train.Optimizer):    def __init__(self, learning_rate=0.001, beta2=0.999, use_locking=False, name="MaxProp"):        super(MaxPropOptimizer, self).__init__(use_locking, name)        self._lr = learning_rate        self._beta2 = beta2        self._lr_t = None        self._beta2_t = None    def _prepare(self):        self._lr_t = tf.convert_to_tensor(self._lr, name="learning_rate")        self._beta2_t = tf.convert_to_tensor(self._beta2, name="beta2")    def _create_slots(self, var_list):        for v in var_list:            self._zeros_slot(v, "m", self._name)    def _apply_dense(self, grad, var):        lr_t = tf.cast(self._lr_t, var.dtype.base_dtype)        beta2_t = tf.cast(self._beta2_t, var.dtype.base_dtype)        if var.dtype.base_dtype == tf.float16:            eps = 1e-7        else:            eps = 1e-8        m = self.get_slot(var, "m")        m_t = m.assign(tf.maximum(beta2_t * m + eps, tf.abs(grad)))        g_t = grad / m_t        var_update = tf.assign_sub(var, lr_t * g_t)        return tf.group(*[var_update, m_t])    def _apply_sparse(self, grad, var):        return self._apply_dense(grad, var)def train_speech_to_text_network():    logit = speech_to_text_network()    # CTC loss    indices = tf.where(tf.not_equal(tf.cast(Y, tf.float32), 0.))    target = tf.SparseTensor(indices=indices, values=tf.gather_nd(Y, indices) - 1, dense_shape=tf.cast(tf.shape(Y), tf.int64))    loss = tf.nn.ctc_loss(target, logit, sequence_len, time_major=False)    # optimizer    lr = tf.Variable(0.001, dtype=tf.float32, trainable=False)    optimizer = MaxPropOptimizer(learning_rate=lr, beta2=0.99)    var_list = [t for t in tf.trainable_variables()]    gradient = optimizer.compute_gradients(loss, var_list=var_list)    optimizer_op = optimizer.apply_gradients(gradient)    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        saver = tf.train.Saver(tf.global_variables())        for epoch in range(16):            sess.run(tf.assign(lr, 0.001 * (0.97 ** epoch)))            global pointer            pointer = 0            for batch in range(n_batch):                batches_wavs, batches_labels = get_next_batches(batch_size)                train_loss, _ = sess.run([loss, optimizer_op], feed_dict={X: batches_wavs, Y: batches_labels})                print(epoch, batch, train_loss)            if epoch % 5 == 0:                saver.save(sess, 'speech.module', global_step=epoch)                # 训练train_speech_to_text_network()# 语音识别# 把batch_size改为1def speech_to_text(wav_file):    wav, sr = librosa.load(wav_file, mono=True)    mfcc = np.transpose(np.expand_dims(librosa.feature.mfcc(wav, sr), axis=0), [0, 2, 1])    logit = speech_to_text_network()    saver = tf.train.Saver()    with tf.Session() as sess:        saver.restore(sess, tf.train.latest_checkpoint('.'))        decoded = tf.transpose(logit, perm=[1, 0, 2])        decoded, _ = tf.nn.ctc_beam_search_decoder(decoded, sequence_len, merge_repeated=False)        predict = tf.sparse_to_dense(decoded[0].indices, decoded[0].shape, decoded[0].values) + 1        output = sess.run(decoded, feed_dict={X: mfcc})        # print(output)

总结方法：

$ sudo apt install python-pip$ pip install librosa

1、问题：

no such module named librosa  

解决方法：安装librosa，见librosa官网
在https://github.com/librosa/librosa 中下载librosa包，并运行setup.py文件。

Source
If you’ve downloaded the archive manually from the releases page, you can install using the setuptools script:

tar xzf librosa-VERSION.tar.gz
cd librosa-VERSION/
python setup.py install

2、问题：

from setuptools import setup, find_packages

解决方法：安装setuptools

sudo apt-get install python-setuptools

3、问题：

ImportError: No module named setuptools

解决方法：Linux环境下安装setuptools

sudo apt-get install python-setuptools

4、问题：
dpkg was interrupted, you must manually run 'dpkg --configure -a' to correct the problem. E: _cache->open() failed, please report.
出现该错误其实有很多种可能，但是终其原因就是系统中的某个软件有问题

解决方法：CSDN博客文章

sudo dpkg --configure -a  sudo apt-get update  sudo apt-get upgrade  

最后又在终端出现了这样的一个问题：、
libc6-dev:依赖:libc6(=2.9-4ubuntu6.1) 但是2.9-Ubuntu6已经安装了 E：不能满足依赖关系。不妨试一下-f选项 于是我在终端输入sudo apt-get -f install

问题得到解决！

5、问题：

ImportError: No module named numpy

解决方法：

sudo apt-get install python-numpy

6、问题：

warning: no files found matching '*.pxd' under directory 'resampy'resampy/interp.c:24:20: fatal error: Python.h: No such file or directorycompilation terminated.error: Setup script exited with error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

解决办法：不使用问题1的解决方法，改用pypi

pypi
The simplest way to install librosa is through the Python Package Index (PyPI). This will ensure that all required dependencies are fulfilled. This can be achieved by executing the following command:

pip install librosa

7、问题：没有安装pip
正确的解决方法：

sudo apt install python-pip

错误的解决方法：运行以下命令 (需要管理员权限): 详见博客。

$ sudo wget https://bootstrap.pypa.io/get-pip.py$ python get-pip.py$ pip -V　　#查看pip版本

pip install librosa

8、问题：

Collecting scikit-learn>=0.14.0 (from librosa)  Downloading scikit_learn-0.18.1-cp27-cp27mu-manylinux1_x86_64.whl (11.6MB)    39% |████████████▋                   | 4.6MB 22kB/s eta 0:05:10Exception:...ReadTimeoutError: HTTPSConnectionPool(host='pypi.python.org', port=443): Read timed out.

因为从librosa里下载速度太慢，所以超时报错，
解决方法：直接下载+设置超时报错时间

pip --default-timeout=100 install -U scikit-learn

但是搞了一圈，又回到了问题1：

no such module named libraso

问题的根本在使用docker运行tensorflow程序，无法导入其他的module，比如libraso。于是改变方法：

解决方法：使用pycharm同时导入tensorflow包和libroso包
文件：pycharm-professional-2017.1.1.tar.gz
激活服务器：http://pycharm.tyr.gift:8888
在解压后的bin文件内运行pycharm.sh文件，启动pycharm。

sudo ./pycharm.sh 

在file-setting中导入tensorflow1.2即可。
运行结果如下：