質問編集履歴

ソースコードの追記

2020/01/21 01:04

投稿

birdbeetle

スコア4

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -32,6 +32,406 @@
 ```python
+import keras
+from keras.models import Model
+from keras.layers import Input, Dense, Dropout, Activation
+from keras.layers import Conv2D, GlobalAveragePooling2D
+from keras.layers import BatchNormalization, Add
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+from keras.models import load_model
+# dataset files
+print("\n***********************************************")
+print("dataset files")
+train_files = ["esc_melsp_train_raw.npz",
+               "esc_melsp_train_ss.npz",
+               "esc_melsp_train_st.npz",
+               "esc_melsp_train_wn.npz",
+               "esc_melsp_train_com.npz"]
+test_file = "esc_melsp_test.npz"
+train_num = 1500
+test_num = 500
+# define dataset placeholders
+print("\n***********************************************")
+print("define dataset placeholders")
+x_train = np.zeros(freq * time * train_num * len(train_files)).reshape(train_num * len(train_files), freq, time)
+y_train = np.zeros(train_num * len(train_files))
+# load dataset
+print("\n***********************************************")
+print("load dataset")
+for i in range(len(train_files)):
+    data = np.load(train_files[i])
+    x_train[i * train_num:(i + 1) * train_num] = data["x"]
+    y_train[i * train_num:(i + 1) * train_num] = data["y"]
+# load test dataset
+print("\n***********************************************")
+print("load test dataset")
+test_data = np.load(test_file)
+x_test = test_data["x"]
+y_test = test_data["y"]
+# redefine target data into one hot vector
+print("\n***********************************************")
+print("redefine target data into one hot vector")
+classes = 50
+y_train = keras.utils.to_categorical(y_train, classes)
+y_test = keras.utils.to_categorical(y_test, classes)
+# reshape training dataset
+print("\n***********************************************")
+print("reshape training dataset")
+x_train = x_train.reshape(train_num * 5, freq, time, 1)
+x_test = x_test.reshape(test_num, freq, time, 1)
+classes = 50
+y_test = keras.utils.to_categorical(y_test, classes)
+x_test = x_test.reshape(test_num, freq, time, 1)
+print("x train:{0}\ny train:{1}\nx test:{2}\ny test:{3}".format(x_train.shape,
+                                                                y_train.shape,
+                                                                x_test.shape,
+                                                                y_test.shape))
+def cba(inputs, filters, kernel_size, strides):
+    x = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
+    x = BatchNormalization()(x)
+    x = Activation("relu")(x)
+    return x
+# define CNN
+print("\n***********************************************")
+print("define CNN")
+inputs = Input(shape=(x_train.shape[1:]))
+x_1 = cba(inputs, filters=32, kernel_size=(1, 8), strides=(1, 2))
+x_1 = cba(x_1, filters=32, kernel_size=(8, 1), strides=(2, 1))
+x_1 = cba(x_1, filters=64, kernel_size=(1, 8), strides=(1, 2))
+x_1 = cba(x_1, filters=64, kernel_size=(8, 1), strides=(2, 1))
+x_2 = cba(inputs, filters=32, kernel_size=(1, 16), strides=(1, 2))
+x_2 = cba(x_2, filters=32, kernel_size=(16, 1), strides=(2, 1))
+x_2 = cba(x_2, filters=64, kernel_size=(1, 16), strides=(1, 2))
+x_2 = cba(x_2, filters=64, kernel_size=(16, 1), strides=(2, 1))
+x_3 = cba(inputs, filters=32, kernel_size=(1, 32), strides=(1, 2))
+x_3 = cba(x_3, filters=32, kernel_size=(32, 1), strides=(2, 1))
+x_3 = cba(x_3, filters=64, kernel_size=(1, 32), strides=(1, 2))
+x_3 = cba(x_3, filters=64, kernel_size=(32, 1), strides=(2, 1))
+x_4 = cba(inputs, filters=32, kernel_size=(1, 64), strides=(1, 2))
+x_4 = cba(x_4, filters=32, kernel_size=(64, 1), strides=(2, 1))
+x_4 = cba(x_4, filters=64, kernel_size=(1, 64), strides=(1, 2))
+x_4 = cba(x_4, filters=64, kernel_size=(64, 1), strides=(2, 1))
+x = Add()([x_1, x_2, x_3, x_4])
+x = cba(x, filters=128, kernel_size=(1, 16), strides=(1, 2))
+x = cba(x, filters=128, kernel_size=(16, 1), strides=(2, 1))
+x = GlobalAveragePooling2D()(x)
+x = Dense(classes)(x)
+x = Activation("softmax")(x)
+model = Model(inputs, x)
+model.summary()
+# initiate Adam optimizer
+print("\n***********************************************")
+print("initiate Adam optimizer")
+opt = keras.optimizers.adam(lr=0.00001, decay=1e-6, amsgrad=True)
+# Let's train the model using Adam with amsgrad
+print("\n***********************************************")
+print("Let's train the model using Adam with amsgrad")
+model.compile(loss='categorical_crossentropy',
+              optimizer=opt,
+              metrics=['accuracy'])
+# directory for model checkpoints
+print("\n***********************************************")
+print("directory for model checkpoints")
+model_dir = "./models"
+if not os.path.exists(model_dir):
+    os.mkdir(model_dir)
+# early stopping and model checkpoint# early
+print("\n***********************************************")
+print("early stopping and model checkpoint# early")
+es_cb = EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')
+chkpt = os.path.join(model_dir, 'esc50_.{epoch:02d}_{val_loss:.4f}_{val_acc:.4f}.hdf5')
+cp_cb = ModelCheckpoint(filepath=chkpt, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
+# between class data generator
+print("\n***********************************************")
+print("between class data generator")
+class MixupGenerator():
+    def __init__(self, x_train, y_train, batch_size=16, alpha=0.2, shuffle=True):
+        self.x_train = x_train
+        self.y_train = y_train
+        self.batch_size = batch_size
+        self.alpha = alpha
+        self.shuffle = shuffle
+        self.sample_num = len(x_train)
+    def __call__(self):
+        while True:
+            indexes = self.__get_exploration_order()
+            itr_num = int(len(indexes) // (self.batch_size * 2))
+            for i in range(itr_num):
+                batch_ids = indexes[i * self.batch_size * 2:(i + 1) * self.batch_size * 2]
+                x, y = self.__data_generation(batch_ids)
+                yield x, y
+    def __get_exploration_order(self):
+        indexes = np.arange(self.sample_num)
+        if self.shuffle:
+            np.random.shuffle(indexes)
+        return indexes
+    def __data_generation(self, batch_ids):
+        _, h, w, c = self.x_train.shape
+        _, class_num = self.y_train.shape
+        x1 = self.x_train[batch_ids[:self.batch_size]]
+        x2 = self.x_train[batch_ids[self.batch_size:]]
+        y1 = self.y_train[batch_ids[:self.batch_size]]
+        y2 = self.y_train[batch_ids[self.batch_size:]]
+        l = np.random.beta(self.alpha, self.alpha, self.batch_size)
+        x_l = l.reshape(self.batch_size, 1, 1, 1)
+        y_l = l.reshape(self.batch_size, 1)
+        x = x1 * x_l + x2 * (1 - x_l)
+        y = y1 * y_l + y2 * (1 - y_l)
+        return x, y
+# train model
+print("\n***********************************************")
+print("train model")
+batch_size = 16
+epochs = 1000
+print("training_generator")
+training_generator = MixupGenerator(x_train, y_train)()
+print("model.fit_generator")
+y_train =
 model.fit_generator(generator=training_generator,
                     steps_per_epoch=x_train.shape[0] // batch_size,
@@ -46,6 +446,36 @@
                     callbacks=[es_cb, cp_cb])
+print("model = load_model")
+model = load_model("./models/esc50_.105_0.8096_0.8200.hdf5")
+# evaluation
+print("\n***********************************************")
+print("evaluation")
+evaluation = model.evaluate(x_test, y_test)
+print(evaluation)
+print("\n***********************************************")
+print("CNN program finish")
 ```
 いくつか同じような状況の方がいらっしゃったみたいで、参考にしたのですがうまくいきませんでした。

初心者マークをつけた

2020/01/21 01:04

投稿

birdbeetle

スコア4

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -48,4 +48,8 @@
 ```
+いくつか同じような状況の方がいらっしゃったみたいで、参考にしたのですがうまくいきませんでした。
 よろしくおねがいします！！！！