In [1]:

    

%pylab
%matplotlib inline
%load_ext watermark


    

    




Using matplotlib backend: MacOSX
Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import glob
import librosa
import tensorflow as tf

%watermark -v -m -p scipy,numpy,sklearn,tensorflow


    

    




/Users/rickypark/anaconda/envs/tfk-notebooks/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
  "`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)






    




CPython 3.5.2
IPython 5.1.0

scipy 0.18.1
numpy 1.11.2
sklearn 0.18
tensorflow 0.10.0

compiler   : GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)
system     : Darwin
release    : 16.1.0
machine    : x86_64
processor  : i386
CPU cores  : 8
interpreter: 64bit

In [3]:

    

sound_names = ["air conditioner","car horn","children playing","dog bark","drilling","engine idling",
               "gun shot","jackhammer","siren","street music"]
sound_data = np.load('urban_sound.npz')
X_data = sound_data['X']
y_data = sound_data['y']
X_data.shape, y_data.shape


    

    
Out[3]:





((8732, 193), (8732, 10))

In [4]:

    

from sklearn.model_selection import train_test_split
X_sub, X_test, y_sub, y_test = train_test_split(X_data, y_data, test_size=0.2)
X_train, X_val, y_train, y_val = train_test_split(X_sub, y_sub, test_size=0.2)
len(X_train), len(X_val), len(X_test), len(y_train), len(y_val), len(y_test)


    

    
Out[4]:





(5588, 1397, 1747, 5588, 1397, 1747)

In [5]:

    

X_train.shape, y_train.shape


    

    
Out[5]:





((5588, 193), (5588, 10))

In [6]:

    

training_epochs = 6000
n_dim = 193
n_classes = 10
n_hidden_units_one = 300
n_hidden_units_two = 200
n_hidden_units_three = 100
learning_rate = 0.1
sd = 1 / np.sqrt(n_dim)


    

In [7]:

    

X = tf.placeholder(tf.float32,[None,n_dim])
Y = tf.placeholder(tf.float32,[None,n_classes])

W_1 = tf.Variable(tf.random_normal([n_dim, n_hidden_units_one], mean=0, stddev=sd), name="w1")
b_1 = tf.Variable(tf.random_normal([n_hidden_units_one], mean=0, stddev=sd), name="b1")
h_1 = tf.nn.sigmoid(tf.matmul(X, W_1) + b_1)

W_2 = tf.Variable(tf.random_normal([n_hidden_units_one, n_hidden_units_two], mean=0, stddev=sd), name="w2")
b_2 = tf.Variable(tf.random_normal([n_hidden_units_two], mean=0, stddev=sd), name="b2")
h_2 = tf.nn.tanh(tf.matmul(h_1, W_2) + b_2)

W_3 = tf.Variable(tf.random_normal([n_hidden_units_two, n_hidden_units_three], mean=0, stddev=sd), name="w3")
b_3 = tf.Variable(tf.random_normal([n_hidden_units_three], mean=0, stddev=sd), name="b3")
h_3 = tf.nn.sigmoid(tf.matmul(h_2, W_3) + b_3)

W = tf.Variable(tf.random_normal([n_hidden_units_three, n_classes], mean=0, stddev=sd), name="w")
b = tf.Variable(tf.random_normal([n_classes], mean = 0, stddev=sd), name="b")
y_ = tf.nn.softmax(tf.matmul(h_3, W) + b)

init = tf.initialize_all_variables()

cost_function = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(y_), reduction_indices=[1])) 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()


    

In [8]:

    

cost_history = np.empty(shape=[1],dtype=float)
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        _,cost = sess.run([optimizer, cost_function], feed_dict={X: X_sub, Y: y_sub})
        cost_history = np.append(cost_history,cost)
    
    print('Validation accuracy: ',round(sess.run(accuracy, feed_dict={X: X_test, Y: y_test}) , 3))
    saver.save(sess, "model_321.ckpt")


    

    




Validation accuracy:  0.867

In [9]:

    

plt.plot(cost_history)


    

    
Out[9]:





[<matplotlib.lines.Line2D at 0x120558e48>]






    

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