Poprawienie ścieżek z bezwzględnych na względne.

This commit is contained in:
s452726 2021-06-22 19:15:39 +02:00
parent dfb26e7950
commit f94b6a3b44
6 changed files with 45 additions and 12 deletions

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@ -1,4 +1,4 @@
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
</project>

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@ -2,7 +2,7 @@
<module type="PYTHON_MODULE" version="4">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

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@ -1,7 +1,12 @@
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<change beforePath="$PROJECT_DIR$/neural_network.py" beforeDir="false" afterPath="$PROJECT_DIR$/neural_network.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/neural_network2.py" beforeDir="false" afterPath="$PROJECT_DIR$/neural_network2.py" afterDir="false" />
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@ -42,7 +47,7 @@
<recent name="C:\Users\micha\Desktop\smieciara" />
</key>
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<module name="smieciara" />
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@ -92,7 +97,7 @@
<envs>
<env name="PYTHONUNBUFFERED" value="1" />
</envs>
<option name="SDK_HOME" value="C:\Users\Natalia\PycharmProjects\untitled2\venv\Scripts\python.exe" />
<option name="SDK_HOME" value="/usr/local/bin/python3.9" />
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@ -106,11 +111,16 @@
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<method v="2" />
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@ -138,6 +148,29 @@
<option name="presentableId" value="Default" />
<updated>1615920190869</updated>
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@ -48,6 +48,6 @@ history = model.fit(x=X_train, y=y_train, validation_split=0.1, epochs=100, batc
#model.evaluate(X_test, y_test)
model.save('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
model.save('./saved_model')

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@ -14,7 +14,7 @@ def b():
a = dff.sample()
pred(a)
model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
model = tf.keras.models.load_model('./saved_model')
def pred(a):

View File

@ -8,15 +8,15 @@ from PIL import Image
img_height = 180
img_width = 180
class_names=['glass','metal','paper','plastic']
model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
model = tf.keras.models.load_model('./saved_model_vers2')
def predict():
path="C:/Users/Natalia/Documents/dane_testowe"
path="./dane_testowe"
files=os.listdir(path)
d=random.choice(files)
im = Image.open("C:/Users/Natalia/Documents/dane_testowe/" + d)
im = Image.open("./dane_testowe/" + d)
im.show()
img = keras.preprocessing.image.load_img(
"C:/Users/Natalia/Documents/dane_testowe/" + d, target_size=(img_height, img_width)
"./dane_testowe/" + d, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch