From d49e60605ea31217e2e554ff5270df98f070e89d Mon Sep 17 00:00:00 2001
From: Silas Dohm <silas@sdohm.xyz>
Date: Tue, 3 Aug 2021 01:09:13 +0200
Subject: [PATCH] better cnn, 81.4% acc, but confusion matrix isnt great)
---
python/w2v_cnn_gen_hdf5.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/python/w2v_cnn_gen_hdf5.py b/python/w2v_cnn_gen_hdf5.py
index 20594e1..fcaab9f 100644
--- a/python/w2v_cnn_gen_hdf5.py
+++ b/python/w2v_cnn_gen_hdf5.py
@@ -10,16 +10,16 @@ from tensorflow.keras.layers import Conv1D,MaxPooling1D,GlobalMaxPooling1D
from tensorflow import keras
modelNN = Sequential()
-#input_shape=((72, 100)))
-modelNN.add(Conv1D(50,kernel_size=5, activation='relu',input_shape=((72, 100))))
+modelNN.add(Conv1D(150,kernel_size=5, activation='relu',input_shape=((72, 100))))
#modelNN.add(MaxPooling1D(pool_size=4))
#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(Conv1D(100,kernel_size=3, activation='relu'))
modelNN.add(MaxPooling1D(pool_size=4))
modelNN.add(Flatten())
-modelNN.add(Dense(25,activation='relu'))
+modelNN.add(Dense(300,activation='relu'))
+modelNN.add(Dense(100,activation='relu'))
#modelNN.add(Dense(50,activation='relu'))
modelNN.add(Dense(10,activation='relu'))
modelNN.add(Dense(3,activation='softmax'))
@@ -30,7 +30,7 @@ from hdf5 import hdf5Generator
path = "G:\\ml\\"
num_rows = 4.8E6
#num_rows = 1E5
-batchSize = 2048
+batchSize = 256
steps = num_rows/batchSize
#early stop
earlystop = keras.callbacks.EarlyStopping(monitor='val_sparse_categorical_accuracy',patience=5,verbose=False,restore_best_weights=True)
@@ -41,8 +41,8 @@ valData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Val")
#%%
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)
-modelNN.save("D:\\ml\\CNN-Classfication-5")
+hist = modelNN.fit(trainData, validation_data=valData, epochs=100,class_weight=cW, steps_per_epoch=steps, validation_steps=int(steps/3),callbacks=cbList)
+modelNN.save("D:\\ml\\CNN-Classfication-6")
#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
testData = hdf5Generator(path + "w2vCNN.hdf5", batchSize, "Test",loop=False)
--
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