Commit 2a08b191 authored by Jakob Overgaard's avatar Jakob Overgaard
Browse files

Merge remote-tracking branch 'origin/validation-split' into validation-split

parents 2f1c15fa e8e542a7
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root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 33}2
root.keras_api.metrics.1"_tf_keras_metric*{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 23}2
\ No newline at end of file
......@@ -3,7 +3,7 @@ import tensorflow as tf
from tensorflow import keras
import pathlib
data_dir = pathlib.Path("../../models/my_model")
data_dir = pathlib.Path("../../models/version1")
model = keras.models.load_model(data_dir)
......
......@@ -95,7 +95,7 @@ history = model.fit(
epochs=epochs
)
model.save("../../models/my_model")
model.save("../../models/version1")
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
......@@ -118,18 +118,3 @@ plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
img = tf.keras.utils.load_img(
"test-tree.jpg", target_size=(img_height, img_width)
)
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
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