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https://github.com/This-weiyuqing/ECG_by_Diffenert_CNN_Test.git
synced 2026-10-18 03:13:39 +08:00
weiyuqing
af187d92e9
By completing the thesis, we have corrected the deficiencies and fixed some bugs.
36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import numpy as np
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import keras
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras.optimizers import SGD
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# 生成虚拟数据
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x_train = np.random.random((100, 100, 100, 3))
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y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
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x_test = np.random.random((20, 100, 100, 3))
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y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)
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model = Sequential()
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# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。
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# 使用 32 个大小为 3x3 的卷积滤波器。
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model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
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model.add(Conv2D(32, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(256, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss='categorical_crossentropy', optimizer=sgd)
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model.fit(x_train, y_train, batch_size=32, epochs=10)
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score = model.evaluate(x_test, y_test, batch_size=32) |