This can work

nn_conv.py

@@ -0,0 +1,130 @@
+#!/usr/bin/python
+#-*-coding:UTF-8-*-
+
+import tensorflow as tf
+from python_speech_features import mfcc,delta
+import numpy as np
+import scipy.io.wavfile as wav
+from read_cep import read_cep
+import os 
+import sklearn.preprocessing
+
+data_in=tf.placeholder(tf.float32,[None,1500,13])
+label=tf.placeholder(tf.float32,[None,63])
+data_img=tf.reshape(data_in,[-1,1500,13,1])
+out_units=63
+labels=[]
+audios=[]
+label_binarizer=""
+
+def find_max_hl(voices):
+    h,l=0,0
+    for voice in voices:
+        a,b=np.array(voice).shape
+        if a>h:
+            h=a
+        if b>l:
+            l=b
+    #return h,l
+    return 1500,13
+def construct_input(voices):
+    h,l=find_max_hl(voices)
+    new_audios=[]
+    for voice in voices:
+        zero_matrix=np.zeros([h,l],np.float32)
+        a,b=np.array(voice).shape
+        for i in range(a):
+            for j in range(b):
+                zero_matrix[i,j]=zero_matrix[i,j]+voice[i,j]
+        new_audios.append(zero_matrix)
+    return new_audios,h,l
+
+def read_wav_mfcc(wav_path):
+    map_path,map_relative=[str(wav_path)+str(x) for x in os.listdir(wav_path) if os.path.isfile(str(wav_path)+str(x))],[y for y in os.listdir(wav_path)]
+    for idx,file_name in enumerate(map_path):
+        fs,audio=wav.read(file_name)
+        a=mfcc(audio,samplerate=fs,nfft=512)
+        audios.append(a)
+        labels.append(int((map_relative[idx].split(".")[0])[:-1]))
+    new_voices,h,l=construct_input(audios)
+    if label_binarizer == "":
+        binarizer = sklearn.preprocessing.LabelBinarizer()
+    else:
+        binarizer=label_binarizer
+    binarizer.fit(range(max(labels)+1))
+    new_ls=binarizer.transform(labels)
+    return new_voices,new_ls
+def conv2d(x,W):
+    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
+def max_op(x):
+    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
+
+
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape,stddev=0.01)#生成一个截断的正态分布
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    initial = tf.constant(0.01,shape=shape)
+    return tf.Variable(initial)
+
+train_feature_batch,train_label_batch=read_wav_mfcc("/home/liuchen/mfcc/MFCC/voice/inputs/train/")
+test_feature_batch,test_label_batch=read_wav_mfcc("/home/liuchen/mfcc/MFCC/voice/inputs/test/")
+
+
+W_conv1=weight_variable([5,5,1,32])
+W_conv2=weight_variable([5,5,32,64])
+
+b_conv1=bias_variable([32])
+b_conv2=bias_variable([64])
+
+conv1=conv2d(data_img,W_conv1)+b_conv1
+pool1=max_op(conv1)
+
+shp=pool1.get_shape()
+flattened_shape=shp[1].value*shp[2].value*shp[3].value
+resh1=tf.reshape(pool1,[-1,flattened_shape])
+
+W_fc1=weight_variable([flattened_shape,out_units])
+
+b_fc1=bias_variable([out_units])
+
+fc1=tf.matmul(resh1,W_fc1)+b_fc1
+fc1=tf.nn.sigmoid(fc1)
+fc1=tf.nn.softmax(fc1)
+
+cross_entropy=tf.reduce_mean(-tf.reduce_sum(label*tf.log(tf.clip_by_value(fc1,1e-10,1.0)),reduction_indices=[1]))
+
+correct_prediction=tf.equal(tf.argmax(fc1,1),tf.argmax(label,1))
+accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
+
+#train_step=tf.train.GradientDescentOptimizer(1e2).minimize(cross_entropy)
+train_step=tf.train.AdamOptimizer(1e-5).minimize(cross_entropy)
+
+init=tf.global_variables_initializer()
+
+
+
+with tf.Session() as sess:
+     sess.run(init)
+     coord=tf.train.Coordinator()
+     threads=tf.train.start_queue_runners(coord=coord)
+     train_exp,train_l=read_wav_mfcc("/home/liuchen/mfcc/MFCC/voice/inputs/train/")
+     test_exp,test_l=read_wav_mfcc("/home/liuchen/mfcc/MFCC/voice/inputs/test/")
+     try:
+         for i in range(5000):
+             #train_exp,train_l=sess.run([train_feature_batch,train_label_batch])
+             #test_exp,test_l=sess.run([test_feature_batch,test_label_batch])
+             #train_exp=tf.convert_to_tensor(train_exp)
+             #test_exp=tf.convert_to_tensor(test_exp)
+             train_step.run({data_in:train_exp,label:train_l})
+             if i%10 ==0:
+                 print("train: ",sess.run(cross_entropy,feed_dict={data_in:train_exp,label:train_l}))
+                 print("test: ",sess.run(cross_entropy,feed_dict={data_in:test_exp,label:test_l}))
+
+                 #print(i,"train",accuracy.eval({data_in:train_exp,label:train_l}))
+                 print(i,"test",accuracy.eval({data_in:test_exp,label:test_l}))
+     finally:
+         coord.request_stop()
+     coord.join(threads)
+     sess.close()