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Commit 75174575 authored by Silas Dohm's avatar Silas Dohm
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decent cnn, not great though

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...@@ -10,21 +10,26 @@ from tensorflow.keras.layers import Conv1D,MaxPooling1D,GlobalMaxPooling1D ...@@ -10,21 +10,26 @@ from tensorflow.keras.layers import Conv1D,MaxPooling1D,GlobalMaxPooling1D
from tensorflow import keras from tensorflow import keras
modelNN = Sequential() modelNN = Sequential()
#input_shape=((72, 100)))
modelNN.add(Conv1D(32, 7, activation='relu',input_shape=((72, 100)))) modelNN.add(Conv1D(50,kernel_size=5, activation='relu',input_shape=((72, 100))))
modelNN.add(Conv1D(32, 7, activation='relu')) #modelNN.add(MaxPooling1D(pool_size=4))
modelNN.add(GlobalMaxPooling1D()) #modelNN.add(Conv1D(250,kernel_size=4, activation='relu'))
modelNN.add(MaxPooling1D(pool_size=4))
modelNN.add(Conv1D(25,kernel_size=3, activation='relu'))
modelNN.add(MaxPooling1D(pool_size=4))
modelNN.add(Flatten()) modelNN.add(Flatten())
modelNN.add(Dense(512,activation='relu')) modelNN.add(Dense(25,activation='relu'))
modelNN.add(Dense(128,activation='relu')) #modelNN.add(Dense(50,activation='relu'))
modelNN.add(Dense(10,activation='relu')) modelNN.add(Dense(10,activation='relu'))
modelNN.add(Dense(3,activation='softmax')) modelNN.add(Dense(3,activation='softmax'))
modelNN.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=["sparse_categorical_accuracy"]) modelNN.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=["sparse_categorical_accuracy"])
modelNN.summary()
#%% #%%
from hdf5 import hdf5Generator from hdf5 import hdf5Generator
path = "G:\\ml\\" path = "G:\\ml\\"
num_rows = 4.8E6 num_rows = 4.8E6
#num_rows = 1E5
batchSize = 2048 batchSize = 2048
steps = num_rows/batchSize steps = num_rows/batchSize
#early stop #early stop
...@@ -37,7 +42,7 @@ valData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Val") ...@@ -37,7 +42,7 @@ valData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Val")
#%% #%%
cW = {0:4.18,1:9.53,2:1.52} cW = {0:4.18,1:9.53,2:1.52}
hist = modelNN.fit(trainData, validation_data=valData, epochs=100,class_weight=cW, steps_per_epoch=steps, validation_steps=steps,callbacks=cbList) hist = modelNN.fit(trainData, validation_data=valData, epochs=100,class_weight=cW, steps_per_epoch=steps, validation_steps=steps,callbacks=cbList)
modelNN.save("D:\\ml\\CNN-Classfication") modelNN.save("D:\\ml\\CNN-Classfication-5")
#modelNN.fit(train,epochs=12,validation_data=val,batch_size=batchSize,steps_per_epoch= num_rows/batchSize,callbacks=cbList,validation_steps=num_rows/batchSize) #modelNN.fit(train,epochs=12,validation_data=val,batch_size=batchSize,steps_per_epoch= num_rows/batchSize,callbacks=cbList,validation_steps=num_rows/batchSize)
# %%eval # %%eval
testData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Test",loop=False) testData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Test",loop=False)
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