Weed Classification in Drone Images with Convolutional Networks¶
Let's start by installing the libraries we will be using:
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!pip install rasterio
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We connect to Google Drive:
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from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
Now, let's install the libraries:
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import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import rasterio
from rasterio.windows import Window
import pandas as pd
import geopandas as gpd
from pylab import rcParams
import matplotlib
rcParams['figure.figsize'] = 18, 16
from sklearn.model_selection import train_test_split
from rasterio.plot import show
from shapely.geometry import box
from rasterio.plot import show
The image and shapefiles to be used are stored in Drive. A point file was collected for each class we have.
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path_img = '/content/drive/MyDrive/Datasets/classificacao_cana_daninhas/AOI_img_rep.tif'
path_classe1 = '/content/drive/MyDrive/Datasets/classificacao_cana_daninhas/Classe_1.shp'
path_classe2 = '/content/drive/MyDrive/Datasets/classificacao_cana_daninhas/Classe_2.shp'
path_classe3 = '/content/drive/MyDrive/Datasets/classificacao_cana_daninhas/Classe_3.shp'
Let's use geopandas to open the Shapefiles and plot them along with the image:
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gdf1 = gpd.read_file(path_classe1)
gdf2 = gpd.read_file(path_classe2)
gdf3 = gpd.read_file(path_classe3)
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fig, ax = plt.subplots(figsize=(20, 20))
with rasterio.open(path_img) as src:
gdf1 = gdf1.to_crs(src.crs.to_dict())
gdf2 = gdf2.to_crs(src.crs.to_dict())
gdf3 = gdf3.to_crs(src.crs.to_dict())
show(src,ax=ax)
gdf1.plot(ax=ax, color='red')
gdf2.plot(ax=ax, color='yellow')
gdf3.plot(ax=ax, color='blue')
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<Axes: >
Then we can start extracting information with the image and shapefiles:
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src = rasterio.open(path_img)
im = src.read()
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im = im.transpose([1,2,0])
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im.shape
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(23835, 8821, 4)
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im = im.astype('uint8')
We create a field called 'id' in all geodataframes to aggregate the sample class, then join them into a single geodataframe:
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gdf1['id'] = 0
gdf2['id'] = 1
gdf3['id'] = 2
For each point we will generate a cut in the image with a size of 256x256 pixels using the point as the center.
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gdf = pd.concat([gdf1,gdf2,gdf3], axis=0)
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img_list = []
label_list = []
for i,pt in gdf.iterrows():
x = pt['geometry'].xy[0][0]
y = pt['geometry'].xy[1][0]
label = pt['id']
row, col = src.index(x,y)
img_path = im[row-64:row+64,col-64:col+64,0:3]
img_list.append(img_path)
label_list.append(label)
We create a variable X to receive the list of images and convert it into a numpy array:
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X = np.array(img_list)
Y = np.array(label_list)
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X.shape
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(300, 128, 128, 3)
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Y.shape
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(300,)
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del im, src, img_list, label_list
Let's draw an example:
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dict_name = {0:'Daninha_tipo_1', 1: 'Daninha_tipo_2', 2:'Normal'}
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i = 20
plt.figure(figsize=[6,6])
plt.title(dict_name[Y[i]])
plt.imshow(X[i])
plt.axis('off')
Out[ ]:
(-0.5, 127.5, 127.5, -0.5)
We normalize the images to the range from 0 to 1:
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X = X/255
Since we are working with categorical data, we need to encode categorical values into binary values so that they are compatible with the expected results of a neural network:
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from sklearn.preprocessing import OneHotEncoder
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Y = Y[:,np.newaxis]
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enc = OneHotEncoder()
enc.fit(Y)
Y = enc.transform(Y).toarray()
First, we will separate the data into training data and test data:
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x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 42)
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n_classes = 3
Now let’s import some Keras functions and create our convolutional network:
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from keras.models import Model, Sequential
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout, Activation
#from tensorflow.keras.optimizers.legacy import Adam
from keras.optimizers import Adam
from keras.activations import relu
from keras.losses import binary_crossentropy
from keras import backend as K
import tensorflow as tf
from keras.models import load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
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Model = Sequential()
Model.add(Conv2D(32,(3,3), kernel_initializer="he_normal", padding='same', input_shape=(x_train.shape[1:])))
Model.add(Activation('relu'))
Model.add(MaxPooling2D(pool_size=(2,2),strides=2))
Model.add(Conv2D(64,(3,3), kernel_initializer="he_normal", padding='same'))
Model.add(Activation('relu'))
Model.add(MaxPooling2D(pool_size=(2,2),strides=2))
Model.add(Conv2D(128,(3,3), kernel_initializer="he_normal", padding='same'))
Model.add(Activation('relu'))
Model.add(MaxPooling2D(pool_size=(2,2),strides=2))
Model.add(Conv2D(256,(3,3), kernel_initializer="he_normal", padding='same'))
Model.add(Activation('relu'))
Model.add(MaxPooling2D(pool_size=(2,2),strides=2))
Model.add(Flatten())
Model.add(Dropout(0.5))
Model.add(Dense(512))
Model.add(Activation('relu'))
Model.add(Dense(n_classes))
Model.add(Activation('softmax'))
Model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.00001), metrics=['accuracy'])
Model.summary()
/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 128, 128, 32) │ 896 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation (Activation) │ (None, 128, 128, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 64, 64, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_1 (Conv2D) │ (None, 64, 64, 64) │ 18,496 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_1 (Activation) │ (None, 64, 64, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 32, 32, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_2 (Conv2D) │ (None, 32, 32, 128) │ 73,856 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_2 (Activation) │ (None, 32, 32, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 16, 16, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_3 (Conv2D) │ (None, 16, 16, 256) │ 295,168 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_3 (Activation) │ (None, 16, 16, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_3 (MaxPooling2D) │ (None, 8, 8, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ flatten (Flatten) │ (None, 16384) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout (Dropout) │ (None, 16384) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense (Dense) │ (None, 512) │ 8,389,120 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_4 (Activation) │ (None, 512) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 3) │ 1,539 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_5 (Activation) │ (None, 3) │ 0 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 8,779,075 (33.49 MB)
Trainable params: 8,779,075 (33.49 MB)
Non-trainable params: 0 (0.00 B)
Then we can train the network:
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history = Model.fit(x = x_train, y= y_train, batch_size=64, epochs=200, verbose=1, shuffle=True, validation_split=0.2)
Epoch 1/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 13s 3s/step - accuracy: 0.3248 - loss: 1.3592 - val_accuracy: 0.3095 - val_loss: 1.2953 Epoch 2/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3150 - loss: 1.3740 - val_accuracy: 0.3095 - val_loss: 1.1284 Epoch 3/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3731 - loss: 1.2137 - val_accuracy: 0.3095 - val_loss: 1.0831 Epoch 4/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.4351 - loss: 1.2063 - val_accuracy: 0.3095 - val_loss: 1.0657 Epoch 5/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.2925 - loss: 1.2964 - val_accuracy: 0.3095 - val_loss: 1.1471 Epoch 6/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3415 - loss: 1.1785 - val_accuracy: 0.3095 - val_loss: 1.1583 Epoch 7/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3850 - loss: 1.1939 - val_accuracy: 0.3095 - val_loss: 1.0602 Epoch 8/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.4182 - loss: 1.1891 - val_accuracy: 0.3095 - val_loss: 1.0094 Epoch 9/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.3642 - loss: 1.1492 - val_accuracy: 0.3095 - val_loss: 1.0363 Epoch 10/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.4359 - loss: 1.1069 - val_accuracy: 0.3095 - val_loss: 1.0705 Epoch 11/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.4399 - loss: 1.0786 - val_accuracy: 0.3095 - val_loss: 1.0286 Epoch 12/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.4095 - loss: 1.1609 - val_accuracy: 0.3810 - val_loss: 0.9967 Epoch 13/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.4853 - loss: 1.0309 - val_accuracy: 0.3810 - val_loss: 0.9848 Epoch 14/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.4589 - loss: 1.1120 - val_accuracy: 0.3810 - val_loss: 0.9961 Epoch 15/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.5029 - loss: 1.0291 - val_accuracy: 0.3333 - val_loss: 0.9943 Epoch 16/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.5907 - loss: 0.9697 - val_accuracy: 0.3333 - val_loss: 0.9820 Epoch 17/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.5196 - loss: 0.9942 - val_accuracy: 0.4524 - val_loss: 0.9582 Epoch 18/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.5472 - loss: 0.9743 - val_accuracy: 0.4048 - val_loss: 0.9655 Epoch 19/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.4665 - loss: 1.0591 - val_accuracy: 0.4048 - val_loss: 0.9692 Epoch 20/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.5383 - loss: 0.9812 - val_accuracy: 0.4524 - val_loss: 0.9354 Epoch 21/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.5755 - loss: 0.9465 - val_accuracy: 0.4762 - val_loss: 0.9225 Epoch 22/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.6240 - loss: 0.8641 - val_accuracy: 0.5000 - val_loss: 0.8997 Epoch 23/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.5767 - loss: 0.8785 - val_accuracy: 0.5476 - val_loss: 0.8799 Epoch 24/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.5827 - loss: 0.8510 - val_accuracy: 0.5238 - val_loss: 0.8884 Epoch 25/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.6356 - loss: 0.8566 - val_accuracy: 0.4762 - val_loss: 0.9203 Epoch 26/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.5807 - loss: 0.8214 - val_accuracy: 0.5000 - val_loss: 0.9074 Epoch 27/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.5875 - loss: 0.8792 - val_accuracy: 0.6190 - val_loss: 0.8287 Epoch 28/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.6003 - loss: 0.8387 - val_accuracy: 0.6190 - val_loss: 0.7818 Epoch 29/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.7036 - loss: 0.7621 - val_accuracy: 0.6190 - val_loss: 0.7973 Epoch 30/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.6242 - loss: 0.8068 - val_accuracy: 0.5238 - val_loss: 0.8770 Epoch 31/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.6270 - loss: 0.8070 - val_accuracy: 0.5000 - val_loss: 0.8993 Epoch 32/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.6438 - loss: 0.7972 - val_accuracy: 0.5714 - val_loss: 0.8511 Epoch 33/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7264 - loss: 0.7072 - val_accuracy: 0.6429 - val_loss: 0.7576 Epoch 34/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.7068 - loss: 0.7372 - val_accuracy: 0.6905 - val_loss: 0.7170 Epoch 35/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7519 - loss: 0.6713 - val_accuracy: 0.6429 - val_loss: 0.7615 Epoch 36/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7136 - loss: 0.6535 - val_accuracy: 0.5714 - val_loss: 0.8360 Epoch 37/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.6900 - loss: 0.7140 - val_accuracy: 0.5476 - val_loss: 0.8153 Epoch 38/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.7577 - loss: 0.6225 - val_accuracy: 0.6429 - val_loss: 0.7115 Epoch 39/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7687 - loss: 0.6408 - val_accuracy: 0.6905 - val_loss: 0.6609 Epoch 40/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7499 - loss: 0.6862 - val_accuracy: 0.6429 - val_loss: 0.6999 Epoch 41/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7281 - loss: 0.6057 - val_accuracy: 0.5714 - val_loss: 0.7631 Epoch 42/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7647 - loss: 0.6019 - val_accuracy: 0.5714 - val_loss: 0.7642 Epoch 43/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.7480 - loss: 0.6381 - val_accuracy: 0.6429 - val_loss: 0.7105 Epoch 44/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7519 - loss: 0.6285 - val_accuracy: 0.6667 - val_loss: 0.6697 Epoch 45/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7804 - loss: 0.5639 - val_accuracy: 0.6905 - val_loss: 0.6508 Epoch 46/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7882 - loss: 0.6049 - val_accuracy: 0.6429 - val_loss: 0.6356 Epoch 47/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.7806 - loss: 0.5825 - val_accuracy: 0.6190 - val_loss: 0.7128 Epoch 48/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7568 - loss: 0.5526 - val_accuracy: 0.6190 - val_loss: 0.6556 Epoch 49/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8317 - loss: 0.5256 - val_accuracy: 0.7381 - val_loss: 0.5768 Epoch 50/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8276 - loss: 0.4874 - val_accuracy: 0.7619 - val_loss: 0.5789 Epoch 51/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8268 - loss: 0.5052 - val_accuracy: 0.6905 - val_loss: 0.6518 Epoch 52/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.7432 - loss: 0.5584 - val_accuracy: 0.6429 - val_loss: 0.6802 Epoch 53/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8013 - loss: 0.5013 - val_accuracy: 0.6429 - val_loss: 0.6459 Epoch 54/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.7638 - loss: 0.5428 - val_accuracy: 0.7381 - val_loss: 0.5546 Epoch 55/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8072 - loss: 0.5317 - val_accuracy: 0.8095 - val_loss: 0.5176 Epoch 56/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.8220 - loss: 0.4927 - val_accuracy: 0.7857 - val_loss: 0.5436 Epoch 57/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8209 - loss: 0.4622 - val_accuracy: 0.6905 - val_loss: 0.5978 Epoch 58/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8258 - loss: 0.4646 - val_accuracy: 0.6667 - val_loss: 0.5985 Epoch 59/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.8730 - loss: 0.4307 - val_accuracy: 0.7143 - val_loss: 0.5549 Epoch 60/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8425 - loss: 0.4690 - val_accuracy: 0.7857 - val_loss: 0.5060 Epoch 61/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8464 - loss: 0.4339 - val_accuracy: 0.7381 - val_loss: 0.5364 Epoch 62/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8259 - loss: 0.4217 - val_accuracy: 0.6190 - val_loss: 0.6302 Epoch 63/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8761 - loss: 0.3895 - val_accuracy: 0.6429 - val_loss: 0.5933 Epoch 64/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8405 - loss: 0.4172 - val_accuracy: 0.7381 - val_loss: 0.5198 Epoch 65/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8672 - loss: 0.4176 - val_accuracy: 0.8095 - val_loss: 0.4806 Epoch 66/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8749 - loss: 0.3989 - val_accuracy: 0.8095 - val_loss: 0.4876 Epoch 67/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8641 - loss: 0.3921 - val_accuracy: 0.7619 - val_loss: 0.5165 Epoch 68/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8681 - loss: 0.3525 - val_accuracy: 0.7619 - val_loss: 0.5376 Epoch 69/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8456 - loss: 0.4035 - val_accuracy: 0.7381 - val_loss: 0.5374 Epoch 70/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8838 - loss: 0.3664 - val_accuracy: 0.7857 - val_loss: 0.4860 Epoch 71/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8780 - loss: 0.3706 - val_accuracy: 0.7857 - val_loss: 0.4772 Epoch 72/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8730 - loss: 0.3588 - val_accuracy: 0.8095 - val_loss: 0.4653 Epoch 73/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8632 - loss: 0.3578 - val_accuracy: 0.7857 - val_loss: 0.4707 Epoch 74/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8544 - loss: 0.3709 - val_accuracy: 0.7857 - val_loss: 0.5193 Epoch 75/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8425 - loss: 0.4077 - val_accuracy: 0.7619 - val_loss: 0.5237 Epoch 76/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8730 - loss: 0.3462 - val_accuracy: 0.7857 - val_loss: 0.4495 Epoch 77/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8740 - loss: 0.3845 - val_accuracy: 0.7857 - val_loss: 0.4090 Epoch 78/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8584 - loss: 0.3683 - val_accuracy: 0.8095 - val_loss: 0.4685 Epoch 79/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8476 - loss: 0.3669 - val_accuracy: 0.7857 - val_loss: 0.4774 Epoch 80/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.8730 - loss: 0.3505 - val_accuracy: 0.8095 - val_loss: 0.4434 Epoch 81/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9026 - loss: 0.3347 - val_accuracy: 0.8095 - val_loss: 0.4073 Epoch 82/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9458 - loss: 0.2609 - val_accuracy: 0.8095 - val_loss: 0.3978 Epoch 83/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9025 - loss: 0.2976 - val_accuracy: 0.8095 - val_loss: 0.4230 Epoch 84/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9143 - loss: 0.3036 - val_accuracy: 0.8095 - val_loss: 0.4670 Epoch 85/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9075 - loss: 0.2786 - val_accuracy: 0.8095 - val_loss: 0.4327 Epoch 86/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.8712 - loss: 0.3380 - val_accuracy: 0.8333 - val_loss: 0.3707 Epoch 87/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9183 - loss: 0.2752 - val_accuracy: 0.8333 - val_loss: 0.3977 Epoch 88/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9281 - loss: 0.2605 - val_accuracy: 0.8333 - val_loss: 0.4004 Epoch 89/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8878 - loss: 0.3105 - val_accuracy: 0.8333 - val_loss: 0.3774 Epoch 90/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9006 - loss: 0.2863 - val_accuracy: 0.8333 - val_loss: 0.3489 Epoch 91/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9243 - loss: 0.2653 - val_accuracy: 0.8333 - val_loss: 0.3662 Epoch 92/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9322 - loss: 0.2667 - val_accuracy: 0.8333 - val_loss: 0.3905 Epoch 93/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9163 - loss: 0.2416 - val_accuracy: 0.8333 - val_loss: 0.4042 Epoch 94/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.8938 - loss: 0.2790 - val_accuracy: 0.8333 - val_loss: 0.3723 Epoch 95/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9409 - loss: 0.2417 - val_accuracy: 0.8333 - val_loss: 0.3890 Epoch 96/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9016 - loss: 0.2517 - val_accuracy: 0.8333 - val_loss: 0.3939 Epoch 97/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9085 - loss: 0.2527 - val_accuracy: 0.8333 - val_loss: 0.3723 Epoch 98/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9460 - loss: 0.2339 - val_accuracy: 0.8333 - val_loss: 0.3544 Epoch 99/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9430 - loss: 0.2584 - val_accuracy: 0.8333 - val_loss: 0.3576 Epoch 100/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9546 - loss: 0.1880 - val_accuracy: 0.8333 - val_loss: 0.3764 Epoch 101/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9330 - loss: 0.2297 - val_accuracy: 0.8333 - val_loss: 0.3543 Epoch 102/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9478 - loss: 0.2009 - val_accuracy: 0.8333 - val_loss: 0.3391 Epoch 103/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9488 - loss: 0.1946 - val_accuracy: 0.8571 - val_loss: 0.3388 Epoch 104/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9381 - loss: 0.2188 - val_accuracy: 0.8095 - val_loss: 0.3829 Epoch 105/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9056 - loss: 0.2443 - val_accuracy: 0.8095 - val_loss: 0.3729 Epoch 106/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9528 - loss: 0.1766 - val_accuracy: 0.8333 - val_loss: 0.3301 Epoch 107/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9164 - loss: 0.2099 - val_accuracy: 0.8333 - val_loss: 0.3147 Epoch 108/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9243 - loss: 0.2182 - val_accuracy: 0.8333 - val_loss: 0.3592 Epoch 109/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9409 - loss: 0.1960 - val_accuracy: 0.8333 - val_loss: 0.3618 Epoch 110/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9233 - loss: 0.2049 - val_accuracy: 0.8571 - val_loss: 0.3112 Epoch 111/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9557 - loss: 0.1915 - val_accuracy: 0.8810 - val_loss: 0.2830 Epoch 112/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9458 - loss: 0.1822 - val_accuracy: 0.8333 - val_loss: 0.3151 Epoch 113/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9321 - loss: 0.1987 - val_accuracy: 0.8333 - val_loss: 0.3489 Epoch 114/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9428 - loss: 0.1912 - val_accuracy: 0.8333 - val_loss: 0.3334 Epoch 115/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.9507 - loss: 0.1553 - val_accuracy: 0.8571 - val_loss: 0.3059 Epoch 116/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9636 - loss: 0.1858 - val_accuracy: 0.8571 - val_loss: 0.3006 Epoch 117/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9517 - loss: 0.1799 - val_accuracy: 0.8571 - val_loss: 0.3081 Epoch 118/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9478 - loss: 0.1722 - val_accuracy: 0.8571 - val_loss: 0.2861 Epoch 119/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9528 - loss: 0.1632 - val_accuracy: 0.8571 - val_loss: 0.2922 Epoch 120/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9645 - loss: 0.1443 - val_accuracy: 0.8571 - val_loss: 0.3115 Epoch 121/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9528 - loss: 0.1762 - val_accuracy: 0.8333 - val_loss: 0.3154 Epoch 122/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9351 - loss: 0.1914 - val_accuracy: 0.8571 - val_loss: 0.2961 Epoch 123/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9144 - loss: 0.2086 - val_accuracy: 0.8571 - val_loss: 0.2957 Epoch 124/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9507 - loss: 0.1539 - val_accuracy: 0.8571 - val_loss: 0.2667 Epoch 125/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9390 - loss: 0.1670 - val_accuracy: 0.8810 - val_loss: 0.2702 Epoch 126/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9764 - loss: 0.1476 - val_accuracy: 0.8571 - val_loss: 0.2958 Epoch 127/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9448 - loss: 0.1737 - val_accuracy: 0.8571 - val_loss: 0.2969 Epoch 128/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9586 - loss: 0.1379 - val_accuracy: 0.8810 - val_loss: 0.2789 Epoch 129/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9429 - loss: 0.1725 - val_accuracy: 0.8810 - val_loss: 0.2599 Epoch 130/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9421 - loss: 0.1554 - val_accuracy: 0.8571 - val_loss: 0.2789 Epoch 131/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9488 - loss: 0.1467 - val_accuracy: 0.8571 - val_loss: 0.3073 Epoch 132/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9469 - loss: 0.1669 - val_accuracy: 0.8571 - val_loss: 0.2866 Epoch 133/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9686 - loss: 0.1243 - val_accuracy: 0.8571 - val_loss: 0.2712 Epoch 134/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9469 - loss: 0.1548 - val_accuracy: 0.8571 - val_loss: 0.2865 Epoch 135/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9449 - loss: 0.1635 - val_accuracy: 0.8333 - val_loss: 0.3262 Epoch 136/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9577 - loss: 0.1480 - val_accuracy: 0.8571 - val_loss: 0.2963 Epoch 137/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9478 - loss: 0.1794 - val_accuracy: 0.9048 - val_loss: 0.2590 Epoch 138/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9597 - loss: 0.1269 - val_accuracy: 0.9048 - val_loss: 0.2591 Epoch 139/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9646 - loss: 0.1503 - val_accuracy: 0.8810 - val_loss: 0.2755 Epoch 140/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9705 - loss: 0.1312 - val_accuracy: 0.8571 - val_loss: 0.2874 Epoch 141/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9635 - loss: 0.1380 - val_accuracy: 0.8810 - val_loss: 0.2848 Epoch 142/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9587 - loss: 0.1212 - val_accuracy: 0.8810 - val_loss: 0.2800 Epoch 143/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9587 - loss: 0.1337 - val_accuracy: 0.8810 - val_loss: 0.2783 Epoch 144/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9636 - loss: 0.1220 - val_accuracy: 0.8810 - val_loss: 0.2682 Epoch 145/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9518 - loss: 0.1478 - val_accuracy: 0.8571 - val_loss: 0.2840 Epoch 146/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9449 - loss: 0.1348 - val_accuracy: 0.8571 - val_loss: 0.2774 Epoch 147/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9586 - loss: 0.1335 - val_accuracy: 0.8810 - val_loss: 0.2573 Epoch 148/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9616 - loss: 0.1219 - val_accuracy: 0.9286 - val_loss: 0.2399 Epoch 149/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9469 - loss: 0.1470 - val_accuracy: 0.8810 - val_loss: 0.2659 Epoch 150/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9469 - loss: 0.1356 - val_accuracy: 0.8571 - val_loss: 0.2751 Epoch 151/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9774 - loss: 0.1138 - val_accuracy: 0.8571 - val_loss: 0.2669 Epoch 152/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9547 - loss: 0.1096 - val_accuracy: 0.8810 - val_loss: 0.2537 Epoch 153/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9734 - loss: 0.1014 - val_accuracy: 0.9048 - val_loss: 0.2478 Epoch 154/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9891 - loss: 0.0952 - val_accuracy: 0.9048 - val_loss: 0.2437 Epoch 155/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9734 - loss: 0.1321 - val_accuracy: 0.9286 - val_loss: 0.2350 Epoch 156/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9538 - loss: 0.1270 - val_accuracy: 0.8810 - val_loss: 0.2463 Epoch 157/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9725 - loss: 0.0994 - val_accuracy: 0.8810 - val_loss: 0.2472 Epoch 158/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9626 - loss: 0.1274 - val_accuracy: 0.8571 - val_loss: 0.2579 Epoch 159/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9567 - loss: 0.1136 - val_accuracy: 0.8810 - val_loss: 0.2626 Epoch 160/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9862 - loss: 0.0792 - val_accuracy: 0.8810 - val_loss: 0.2544 Epoch 161/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9764 - loss: 0.1075 - val_accuracy: 0.9048 - val_loss: 0.2405 Epoch 162/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9822 - loss: 0.1132 - val_accuracy: 0.8810 - val_loss: 0.2515 Epoch 163/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9940 - loss: 0.0718 - val_accuracy: 0.8810 - val_loss: 0.2624 Epoch 164/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9685 - loss: 0.1203 - val_accuracy: 0.8810 - val_loss: 0.2552 Epoch 165/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9882 - loss: 0.0877 - val_accuracy: 0.8810 - val_loss: 0.2464 Epoch 166/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9744 - loss: 0.1245 - val_accuracy: 0.9048 - val_loss: 0.2413 Epoch 167/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9754 - loss: 0.0989 - val_accuracy: 0.9048 - val_loss: 0.2368 Epoch 168/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9823 - loss: 0.0906 - val_accuracy: 0.8810 - val_loss: 0.2529 Epoch 169/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9764 - loss: 0.0921 - val_accuracy: 0.9048 - val_loss: 0.2462 Epoch 170/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9734 - loss: 0.0831 - val_accuracy: 0.9286 - val_loss: 0.2363 Epoch 171/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9734 - loss: 0.0873 - val_accuracy: 0.9048 - val_loss: 0.2414 Epoch 172/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9823 - loss: 0.0957 - val_accuracy: 0.8810 - val_loss: 0.2553 Epoch 173/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9685 - loss: 0.1258 - val_accuracy: 0.8810 - val_loss: 0.2486 Epoch 174/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9625 - loss: 0.1035 - val_accuracy: 0.9048 - val_loss: 0.2365 Epoch 175/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9862 - loss: 0.0960 - val_accuracy: 0.9286 - val_loss: 0.2247 Epoch 176/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9813 - loss: 0.0995 - val_accuracy: 0.9286 - val_loss: 0.2247 Epoch 177/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9803 - loss: 0.0790 - val_accuracy: 0.9048 - val_loss: 0.2449 Epoch 178/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9813 - loss: 0.0973 - val_accuracy: 0.8810 - val_loss: 0.2464 Epoch 179/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9774 - loss: 0.0826 - val_accuracy: 0.9048 - val_loss: 0.2336 Epoch 180/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9882 - loss: 0.0684 - val_accuracy: 0.9286 - val_loss: 0.2189 Epoch 181/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9764 - loss: 0.0824 - val_accuracy: 0.9286 - val_loss: 0.2162 Epoch 182/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9744 - loss: 0.0806 - val_accuracy: 0.9048 - val_loss: 0.2326 Epoch 183/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9774 - loss: 0.1008 - val_accuracy: 0.8810 - val_loss: 0.2510 Epoch 184/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9970 - loss: 0.0702 - val_accuracy: 0.8810 - val_loss: 0.2481 Epoch 185/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9695 - loss: 0.1143 - val_accuracy: 0.9286 - val_loss: 0.2238 Epoch 186/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9794 - loss: 0.0738 - val_accuracy: 0.9286 - val_loss: 0.2194 Epoch 187/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9862 - loss: 0.0633 - val_accuracy: 0.9286 - val_loss: 0.2164 Epoch 188/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9813 - loss: 0.0860 - val_accuracy: 0.9048 - val_loss: 0.2305 Epoch 189/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9636 - loss: 0.0859 - val_accuracy: 0.8810 - val_loss: 0.2476 Epoch 190/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9735 - loss: 0.0965 - val_accuracy: 0.8810 - val_loss: 0.2597 Epoch 191/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9882 - loss: 0.0683 - val_accuracy: 0.9048 - val_loss: 0.2412 Epoch 192/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9557 - loss: 0.1013 - val_accuracy: 0.9286 - val_loss: 0.2203 Epoch 193/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9794 - loss: 0.0774 - val_accuracy: 0.9286 - val_loss: 0.2173 Epoch 194/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9833 - loss: 0.0715 - val_accuracy: 0.9286 - val_loss: 0.2244 Epoch 195/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9774 - loss: 0.0777 - val_accuracy: 0.9048 - val_loss: 0.2349 Epoch 196/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9951 - loss: 0.0634 - val_accuracy: 0.9048 - val_loss: 0.2407 Epoch 197/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step - accuracy: 0.9813 - loss: 0.0732 - val_accuracy: 0.9048 - val_loss: 0.2393 Epoch 198/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - accuracy: 0.9872 - loss: 0.0661 - val_accuracy: 0.9286 - val_loss: 0.2264 Epoch 199/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9951 - loss: 0.0570 - val_accuracy: 0.9048 - val_loss: 0.2319 Epoch 200/200 3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9823 - loss: 0.0752 - val_accuracy: 0.9048 - val_loss: 0.2341
After finishing the training, we will create the accuracy and loss graphs per iteration:
In [ ]:
import seaborn as sns
plt.style.use('fivethirtyeight')
rcParams['figure.figsize'] = 18, 6
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='lower right')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()
Now we are going to predict the classes of the images in the dataset that we separated for testing.
In [ ]:
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
In [ ]:
predict = Model.predict(x_test)
3/3 ━━━━━━━━━━━━━━━━━━━━ 2s 399ms/step
In [ ]:
pred = np.argmax(predict, axis=1)
In [ ]:
true = np.argmax(y_test, axis=1)
We then calculate the overall accuracy of the test data:
In [ ]:
accuracy = accuracy_score(true,pred)
print(accuracy)
0.8888888888888888
We can also use the classification report to see some metrics by class:
In [ ]:
print(classification_report(true, pred))
precision recall f1-score support
0 0.86 0.89 0.87 35
1 1.00 0.96 0.98 28
2 0.81 0.81 0.81 27
accuracy 0.89 90
macro avg 0.89 0.89 0.89 90
weighted avg 0.89 0.89 0.89 90
In [ ]:
cm = confusion_matrix(true, pred)
print(cm)
[[31 0 4] [ 0 27 1] [ 5 0 22]]
In [ ]:
class_list = ['Daninha_tipo_1', 'Daninha_tipo_2','Normal']
columns = class_list
r1 = pd.DataFrame(data=cm, columns=columns, index=columns)
fig, ax = plt.subplots(figsize=(10,8))
ax = sns.heatmap(r1, annot=True, annot_kws={"size": 18},fmt='d',cmap="inferno_r", cbar = False)
#for t in ax.texts: t.set_text(t.get_text() + " %")
ax.tick_params(labelsize=16)
ax.set_ylabel('Verdadeiro')
ax.set_xlabel('Predito')
Out[ ]:
Text(0.5, 25.583333333333286, 'Predito')
Now let's divide the whole image into 128x128 patches and classify each of these parts of the image:
In [ ]:
path_img_to_pred = '/content/drive/MyDrive/Datasets/classificacao_cana_daninhas/AOI_img_rep.tif'
path_split = "/content/split_img"
if not os.path.isdir(path_split):
os.mkdir(path_split)
In [ ]:
src = rasterio.open(path_img_to_pred)
out_meta = src.meta.copy()
qtd = 0
for n in range((src.meta['width']//128)):
for m in range((src.meta['height']//128)):
x = ((n*128))
y = ((m*128))
window = Window(x,y,128,128)
win_transform = src.window_transform(window)
arr_win = src.read(window=window)
if arr_win.max() != 0:
qtd = qtd + 1
path_exp_img = os.path.join(path_split, 'img_' + str(qtd) + '.tif')
out_meta.update({"driver": "GTiff","height": arr_win.shape[1],"width": arr_win.shape[2], "compress":'lzw', "transform":win_transform})
with rasterio.open(path_exp_img, 'w', **out_meta) as dst:
for i, layer in enumerate(arr_win, start=1):
dst.write_band(i, layer.reshape(-1, layer.shape[-1]))
print('Create img: ' + str(qtd))
del arr_win
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In [ ]:
n = [f for f in os.listdir(path_split)]
In [ ]:
df_full = pd.DataFrame([])
im_list = []
geom_list = []
for path_img in n:
img = []
path_full = os.path.join(path_split,path_img)
ds = rasterio.open(path_full, 'r')
im = ds.read()
im = im.transpose([1,2,0])
im = im[:,:,0:3]
im = im/255
im_list.append(im)
geom = box(*ds.bounds)
geom_list.append(geom)
In [ ]:
im_list = np.array(im_list)
In [ ]:
predict = Model.predict(im_list)
predict = np.argmax(predict, axis=1)
197/197 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step
In [ ]:
df_full = gpd.GeoDataFrame({'classe':predict,"geometry":geom_list})
The generated result is a dataframe with polygons representing the areas of the image:
In [ ]:
df_full
Out[ ]:
| classe | geometry | |
|---|---|---|
| 0 | 2 | POLYGON ((803227.247 7689807.152, 803227.247 7... |
| 1 | 0 | POLYGON ((803386.417 7689915.670, 803386.417 7... |
| 2 | 2 | POLYGON ((803400.887 7689430.955, 803400.887 7... |
| 3 | 2 | POLYGON ((803437.061 7689655.226, 803437.061 7... |
| 4 | 2 | POLYGON ((803198.307 7690422.090, 803198.307 7... |
| ... | ... | ... |
| 6272 | 2 | POLYGON ((803487.706 7689568.411, 803487.706 7... |
| 6273 | 2 | POLYGON ((803343.007 7689322.436, 803343.007 7... |
| 6274 | 2 | POLYGON ((803321.302 7690262.929, 803321.302 7... |
| 6275 | 2 | POLYGON ((803400.887 7689590.115, 803400.887 7... |
| 6276 | 2 | POLYGON ((803393.652 7689445.424, 803393.652 7... |
6277 rows × 2 columns
In [ ]:
df_full = df_full.set_crs(src.crs.to_dict())
/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6 in_crs_string = _prepare_from_proj_string(in_crs_string)
We plot this GeoDataFrame to see the result:
In [ ]:
ax = df_full.plot("classe",legend=True, cmap='RdYlGn',categorical=True, legend_kwds={"loc": "center left", "bbox_to_anchor": (1, 0.5)})
def replace_legend_items(legend, mapping):
for txt in legend.texts:
for k,v in mapping.items():
if txt.get_text() == str(k):
txt.set_text(v)
replace_legend_items(ax.get_legend(), dict_name)
In [ ]:
df_full.to_file('/content/veg_classification.shp')