keras model code 추가

code/keras_model.py

@@ -0,0 +1,130 @@
+import librosa
+import scipy.signal as signal
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+import numpy as np
+
+x_train = []
+y_train = []
+chords = ['C', 'D', 'E', 'F', 'G', 'A', 'B', 'HC']
+path = '/Users/seungwoomun/Documents/MYC/train/Grand Piano/'
+paths = '/Users/seungwoomun/Documents/MYC/train/Grand Piano/C_1.wav'
+
+# file_name = ["C_1.wav", "D_1.wav"]
+# 도는 [1,0], 레는 [0,1]로 설정한다
+lable = [[1, 0, 0, 0, 0, 0, 0, 0], # 도 C
+        [0, 1, 0, 0, 0, 0, 0, 0],  # 레 D
+        [0, 0, 1, 0, 0, 0, 0, 0],  # 미 E
+        [0, 0, 0, 1, 0, 0, 0, 0],  # 파 F
+        [0, 0, 0, 0, 1, 0, 0, 0],  # 솔 G
+        [0, 0, 0, 0, 0, 1, 0, 0],  # 라 A
+        [0, 0, 0, 0, 0, 0, 1, 0],  # 시 B
+        [0, 0, 0, 0, 0, 0, 0, 1]]  # 도 HC
+
+# train data 생성
+for index in range(len(chords)):
+    audio_sample, sampling_rate = librosa.load(path + chords[index] + '_1.wav', sr = 44100)
+    input_data = np.abs(librosa.stft(audio_sample, n_fft = 2048, hop_length = 1024, win_length = 2048, window=signal.hann))
+    
+    # input_data의 배열 형태를 알아본다
+    shape = np.shape(input_data)
+    nb_samples = shape[0]
+    nb_windows = shape[1]
+    
+    # [nb_samples][nb_windows] 형태이므로, 딥러닝을 수행하기 위한 배열 형태 [nb_windows][nb_samples]로 맞춰준다
+    input_data = input_data.T
+    
+    # 학습용 레이블 배열을 생성한다
+    lable_tmp = [lable[index] for row in range(nb_windows)]
+    
+    # 하나의 배열로 합친다    
+    if index == 0:
+        x_train = input_data
+        y_train = lable_tmp
+    else:
+        x_train = np.r_[x_train, input_data]
+        y_train = np.r_[y_train, lable_tmp]
+
+# test data 생성
+for indexs in range(len(chords)):
+    audio_sample2, sampling_rate2 = librosa.load(path + chords[indexs] + '_3.wav', sr = 44100)
+    input_data2 = np.abs(librosa.stft(audio_sample2, n_fft = 2048, hop_length = 1024, win_length = 2048, window=signal.hann))
+    
+    # input_data의 배열 형태를 알아본다
+    shape2 = np.shape(input_data2)
+    nb_samples2 = shape2[0]
+    nb_windows2 = shape2[1]
+    
+    # [nb_samples][nb_windows] 형태이므로, 딥러닝을 수행하기 위한 배열 형태 [nb_windows][nb_samples]로 맞춰준다
+    input_data2 = input_data2.T
+    
+    # 학습용 레이블 배열을 생성한다
+    lable_tmp2 = [lable[indexs] for row in range(nb_windows2)]
+    
+    # 하나의 배열로 합친다    
+    if indexs == 0:
+        x_test = input_data2
+        y_test = lable_tmp2
+    else:
+        x_test = np.r_[x_test, input_data2]
+        y_test = np.r_[y_test, lable_tmp2]
+
+# print(x_train.shape)
+# print(y_train.shape)
+# print(x_test.shape)
+# print(y_test.shape)
+
+# model 생성
+model = Sequential()
+
+model.add(Dense(units = 1025, input_dim = 1025)) # input_shape = (513, )
+model.add(Dense(units = 128, activation = 'relu'))
+model.add(Dense(8, activation = 'softmax'))
+# model.add(Dense(128, activation = 'relu'))
+# model.add(Dense(8, input_dim = 128, activation = 'relu'))
+# model.add(Dense(1, activation = 'sigmoid'))
+
+model.summary()
+
+model.compile(optimizer = 'sgd', loss = 'mse', metrics = ['acc'])
+
+# x_train = [[2, 4, 8], [3, 6, 9], [4, 8, 12]]
+# y_train = [[2], [3], [4]]
+# x_test = [[2, 4, 8]]
+
+# 학습
+model.fit(x_train, y_train, epochs = 500, verbose = 1, validation_data = (x_test, y_test)) # batch_size = 257
+
+# 평가
+print("#######  Model 평가  #######")
+loss, acc = model.evaluate(x_test, y_test)
+print("복원된 모델의 정확도: {:5.2f}%".format(100 * acc))
+
+# 검증
+print("#######  Model 검증  #######")
+for i in range(255, 300) :
+    test = np.array([x_test[i]])
+    # print(test)
+    print(model.predict(test))
+
+# model 저장
+# model.save('DNN_Model_2048_500.h5')
+
+# W = tf.Variable(tf.random.normal([1]), name="weight")
+# b = tf.Variable(tf.random.normal([1]), name="bias")
+
+# hypothesis=x_train*W+b
+
+# cost = tf.reduce_mean(tf.square(hypothesis - y_train))
+# sgd = tf.keras.optimizers.SGD(learning_rate=0.01)
+
+# model = Sequential()
+
+# model.add(Dense(1, input_dim = 1))
+
+# model.compile(loss='mean_squared_error',optimizer=sgd)
+
+# model.fit(x_train,y_train,epochs=10)
+
+# print(model.predict(np.array([5])))