From 16002209bf817feb4fa01149248f2bd0ff8bc0d0 Mon Sep 17 00:00:00 2001 From: s450026 Date: Tue, 26 May 2020 21:23:50 +0200 Subject: [PATCH] Add report --- Podprojekt-CNN-Maksymilian-Kierski.md | 0 .../Data/LogsIMG/accuracy.svg | 1 + src/SubprojectMaksymilianKierski/Data/LogsIMG/loss.svg | 1 + src/SubprojectMaksymilianKierski/PlateRecognition.py | 10 ++++------ 4 files changed, 6 insertions(+), 6 deletions(-) create mode 100644 Podprojekt-CNN-Maksymilian-Kierski.md create mode 100644 src/SubprojectMaksymilianKierski/Data/LogsIMG/accuracy.svg create mode 100644 src/SubprojectMaksymilianKierski/Data/LogsIMG/loss.svg diff --git a/Podprojekt-CNN-Maksymilian-Kierski.md b/Podprojekt-CNN-Maksymilian-Kierski.md new file mode 100644 index 0000000..e69de29 diff --git a/src/SubprojectMaksymilianKierski/Data/LogsIMG/accuracy.svg b/src/SubprojectMaksymilianKierski/Data/LogsIMG/accuracy.svg new file mode 100644 index 0000000..558a85c --- /dev/null +++ b/src/SubprojectMaksymilianKierski/Data/LogsIMG/accuracy.svg @@ -0,0 +1 @@ +0.450.50.550.60.650.70.750.80.850.90.9511.05-1012345678910 \ No newline at end of file diff --git a/src/SubprojectMaksymilianKierski/Data/LogsIMG/loss.svg b/src/SubprojectMaksymilianKierski/Data/LogsIMG/loss.svg new file mode 100644 index 0000000..ea0417e --- /dev/null +++ b/src/SubprojectMaksymilianKierski/Data/LogsIMG/loss.svg @@ -0,0 +1 @@ +-0.100.10.20.30.40.50.60.70.80.91-1012345678910 \ No newline at end of file diff --git a/src/SubprojectMaksymilianKierski/PlateRecognition.py b/src/SubprojectMaksymilianKierski/PlateRecognition.py index 2e4ca9a..d89d5fe 100644 --- a/src/SubprojectMaksymilianKierski/PlateRecognition.py +++ b/src/SubprojectMaksymilianKierski/PlateRecognition.py @@ -9,7 +9,7 @@ import pickle # For creating model import tensorflow as tf from tensorflow.keras.models import Sequential # to use sequential model -from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, \ +from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, \ MaxPooling2D # elements which we need to creat our layers # For analysing model @@ -34,8 +34,8 @@ y = [] # label set layer size | conv layer | Dense layer | 64 | 1 | 0 | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692 64 | 2 | 0 | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462 - 64 | 3 | 0 | loss: 0.2491 - accuracy: 0.9020 - val_loss: 0.3762 - val_accuracy: 0.7949 - 64 | 1 | 1 | loss: 0.0531 - accuracy: 0.9971 - val_loss: 0.4176 - val_accuracy: 0.8205 -> + 64 | 3 | 0 | loss: 0.2491 - accuracy: 0.9020 - val_loss: 0.3762 - val_accuracy: 0.7949 -> + 64 | 1 | 1 | loss: 0.0531 - accuracy: 0.9971 - val_loss: 0.4176 - val_accuracy: 0.8205 64 | 2 | 1 | loss: 0.0644 - accuracy: 0.9798 - val_loss: 0.5606 - val_accuracy: 0.8462 64 | 3 | 1 | loss: 0.1126 - accuracy: 0.9625 - val_loss: 0.5916 - val_accuracy: 0.8205 ''' @@ -103,9 +103,7 @@ def creat_model(): model = Sequential() # initialize our model as a Sequential model - model.add(Conv2D(64, (3, 3), - input_shape=X.shape[ - 1:])) # first convolution layer 64 neurons (filters cuz it a convolutional layer), checking 3px on 3px, of 50px 50px grey img + model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:])) # first convolution layer 64 neurons (filters cuz it a convolutional layer), checking 3px on 3px, of 50px 50px grey img model.add(Activation('relu')) # relu activation function model.add(MaxPooling2D(pool_size=(2, 2))) # max pooling on 2px on 2px conv2 layer to get the max value