135 lines
4.0 KiB
Python
135 lines
4.0 KiB
Python
'''
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Thomas Matlak Avi Vajpeyi, Avery Rapson
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CS 310 Final Project
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Loads the NN saved in the dir 'savedFile'. The function predictmood(input_midi_file)
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takes a midi files in MIDO format and returns if it is happy or sad
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Usage:
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python usingMusicNN.py
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'''
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import tensorflow as tf
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import json
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from mido import MidiFile
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import numpy as np
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import tempfile
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midiFile = "01.mid"
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saveFile = "savedModels/musicModelpy27"
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pianoSize = 128
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print("Bad ass Neural Net being loaded...")
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hm_data = 2000000
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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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x = tf.placeholder('float')
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y = tf.placeholder('float')
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current_epoch = tf.Variable(1)
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hidden_1_layer = {'f_fum':n_nodes_hl1,
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'weight':tf.Variable(tf.random_normal([pianoSize, 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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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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#
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def predictmood(input_midi_file):
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prediction = neural_network_model(x)
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# with open('musicModel.pickle','rb') as f:
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# lexicon = pickle.load(f)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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saver = tf.train.import_meta_graph(saveFile+'.meta')
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saver.restore(sess, saveFile)
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#### CONVERT THE MIDI TO NOTES AND FEATURES (without [0,1])
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#### need it in the [0 112 1 1 0 0 0 ....] format
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mid = input_midi_file
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notes = []
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time = float(0)
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prev = float(0)
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for msg in mid:
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if time >= 10:
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break
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### this time is in seconds, not ticks
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time += msg.time
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if not msg.is_meta:
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### only interested in piano channel
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if msg.channel == 0:
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if msg.type == 'note_on':
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# note in vector form to train on
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note = msg.bytes()
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# only interested in the note #and velocity. note message is in the form of [type, note, velocity]
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note = note[1] #:3]
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# note.append(time - prev)
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prev = time
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notes.append(note)
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noteCount = np.zeros(pianoSize)
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for note in notes:
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noteCount[note] += 1
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noteCount = list(noteCount)
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#features = np.array(list(features))
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# pos: [1,0] , argmax: 0
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# neg: [0,1] , argmax: 1
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result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:[noteCount]}),1)))
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if result[0] == 0:
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return ("Sad")
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elif result[0] == 1:
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return ("Happy")
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# with open('mood.txt', 'w') as outfile:
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# mood_dict = dict()
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# if result[0] == 0:
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# mood_dict = {'Mood': "Happy"}
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# elif result[0] == 1:
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# mood_dict = {'Mood': "Sad"}
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# json.dump(mood_dict, outfile)
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# output.seek(0) #resets the pointer to the data of the file to the start
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# return output
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