159 lines
5.4 KiB
Python
159 lines
5.4 KiB
Python
import tensorflow as tf
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import numpy as np
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import pickle
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import os
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# from tensorflow.examples.tutorials.mnist import input_data
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# mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
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# from createMusicalFeaturesets import create_feature_sets_and_labels
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train_x,train_y,test_x,test_y = pickle.load(open("notesData2.pickle", "rb"))
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saveFile = "savedModels/musicModelpy27"
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n_nodes_hl1 = 1000
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n_nodes_hl2 = 1000
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n_nodes_hl3 = 1000
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n_classes = 2
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batch_size = 10
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hm_epochs = 9
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input_data_size = len(train_x[0])# each train_x instance is one song, and so one lexicon of notes
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print("DEBUG: input data size = "+str(input_data_size))
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x = tf.placeholder('float')
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y = tf.placeholder('float')
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hidden_1_layer = {'f_fum':n_nodes_hl1,
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'weight':tf.Variable(tf.random_normal([128, n_nodes_hl1])),
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'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
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hidden_2_layer = {'f_fum':n_nodes_hl2,
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'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
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'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
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hidden_3_layer = {'f_fum':n_nodes_hl3,
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'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
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'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
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output_layer = {'f_fum':None,
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'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
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'bias':tf.Variable(tf.random_normal([n_classes])),}
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# Nothing changes
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def neural_network_model(data):
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####INPUT LAYER (HIDDEN LAYER 1)
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l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
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l1 = tf.nn.relu(l1)
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####HIDDEN LAYER 2
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l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
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l2 = tf.nn.relu(l2)
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####HIDDEN LAYER 3
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l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
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l3 = tf.nn.relu(l3)
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####OUTPUT LAYER
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output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
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return output
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def train_neural_network(x):
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prediction = neural_network_model(x)
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cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
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optimizer = tf.train.AdamOptimizer().minimize(cost)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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# try:
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# epoch = int(open(tf_log,'r').read().split('\n')[-2])+1
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# print('STARTING EPOCH:',epoch)
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# except:
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# epoch = 1
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batches_run = 0
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epoch = 1
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while epoch <= hm_epochs:
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# if epoch != 1:
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# #saver.restore(sess,'/'+saveFile)
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# print("Should Restore Saved File")
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epoch_loss = 1
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i=0
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while i < len(train_x):
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start = i
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end = i+batch_size
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batch_x = np.array(train_x[start:end])
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batch_y = np.array(train_y[start:end])
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_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
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epoch_loss += c
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i+=batch_size
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batches_run +=1
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print('Batch run:',batches_run,'/',batch_size,'| Epoch:',
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epoch,'| Batch Loss:',c,)
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saver.save(sess, saveFile)
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print("Should Save session in "+ saveFile )
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print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:', epoch_loss)
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# with open(tf_log,'a') as f:
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# f.write(str(epoch)+'\n')
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epoch +=1
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correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
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accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
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print('Trained',len(train_x),'samples.')
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print('Tested',len(test_x),'samples.')
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accPercent = accuracy.eval({x:test_x, y:test_y})*100
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print('Accuracy: '+ str(accPercent)+ '%')
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saver = tf.train.Saver()
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# tf_log = 'tf.log' ## SAVES EPOCH NUMBER
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train_neural_network(x)
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def test_neural_network():
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prediction = neural_network_model(x)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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# for epoch in range(hm_epochs):
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# try:
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# y =2
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# # saver.restore(sess,'/'+saveFile)
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# print("Restoring "+ saveFile )
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# except Exception as e:
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# print(str(e))
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# epoch_loss = 0
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correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
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accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
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## WHEN WE SAVE TESTING DATA SEPARATLY
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# feature_sets = []
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# labels = []
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# counter = 0
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# with open('processed-test-set.csv', buffering=20000) as f:
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# for line in f:
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# try:
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# features = list(eval(line.split('::')[0]))
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# label = list(eval(line.split('::')[1]))
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# feature_sets.append(features)
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# labels.append(label)
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# counter += 1
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# except:
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# pass
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testx = np.array(test_x)
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testy = np.array(test_y)
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counter = len(test_x)
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print(testx,testy)
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print(test_x,test_y)
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print('******RESULTS******')
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print('Tested',counter,'samples.')
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print('Accuracy:', accuracy.eval({x:testx, y:testy}) )
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#test_neural_network()
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print ("\n\n\nFINISHED\n\n\n")
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# x =os.remove("tf.log")
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# print("removed :" + str(x))
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