diff --git a/OCR/BackendOCR.py b/OCR/BackendOCR.py
index a045d3c1b507e6ba9a017793d8952c0f14edcd14..f88880eb058e23d31a1aba50aa2b827481225a6b 100644
--- a/OCR/BackendOCR.py
+++ b/OCR/BackendOCR.py
@@ -3,6 +3,11 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from matplotlib import pyplot as plt
+
+
#It hide INFO/Warning and Error, as the usually one will be tensorflow reminding me of not having a cuba driver or the fact it has been installed correctly
#both doesnt stop the program completing
@@ -13,13 +18,71 @@ batch_size = 10
train_ds, val_ds = tf.keras.utils.image_dataset_from_directory(
"ImageTest",
labels="inferred",
+ label_mode="categorical",
image_size=image_size,
batch_size=batch_size,
validation_split=0.1,
subset="both",
- seed=1219
+ seed=1219,
+ color_mode="grayscale"
+)
+
+data_augmentation = keras.Sequential(
+ [
+ layers.RandomFlip("horizontal"),
+ layers.RandomRotation(0.1),
+ ]
+)
+
+train_ds = train_ds.map(
+ lambda img, label: (data_augmentation(img), label),
+ num_parallel_calls=tf.data.AUTOTUNE,
+)
+
+# Prefetching samples in GPU memory helps maximize GPU utilization.
+train_ds = train_ds.prefetch(tf.data.AUTOTUNE)
+val_ds = val_ds.prefetch(tf.data.AUTOTUNE)
+
+model = Sequential()
+
+model.add(Dense(400, activation='relu'))
+model.add(layers.Rescaling(1/255))
+model.add(Dense(200, activation='relu'))
+
+model.add(Dense(500, activation='relu'))
+
+model.add(Dense(200, activation='relu'))
+model.add(layers.Flatten())
+model.add(Dense(10, activation='softmax'))
+
+
+epochs = 40
+
+
+model.compile(
+ optimizer=keras.optimizers.Adam(1e-3),
+ loss="binary_crossentropy",
+ metrics=["accuracy"],
+)
+model.fit(
+ train_ds,
+ epochs=epochs,
+ validation_data=val_ds,
)
+img = keras.utils.load_img(
+ "Test2.jpg",
+ target_size=image_size,
+ color_mode="grayscale"
+)
+img_array = keras.utils.img_to_array(img)
+img_array = tf.expand_dims(img_array, 0) # Create batch axis
+
+predictions = model.predict(img_array)
+for v in range(10):
+ print(str(v) + " possible à {0:.2f}".format(predictions[0][v] * 100))
+
#https://keras.io/examples/vision/image_classification_from_scratch/
#https://keras.io/examples/
-#https://www.sitepoint.com/keras-digit-recognition-tutorial/
\ No newline at end of file
+#https://www.sitepoint.com/keras-digit-recognition-tutorial/
+#https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory
\ No newline at end of file
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diff --git a/OCR/StckModel.py b/OCR/StckModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9d3336e90b428eae014e02c4e0defae7aa4e084
--- /dev/null
+++ b/OCR/StckModel.py
@@ -0,0 +1,45 @@
+def make_model(input_shape, num_classes):
+ inputs = keras.Input(shape=input_shape)
+
+ # Entry block
+ x = layers.Rescaling(1.0 / 255)(inputs)
+ x = layers.Conv2D(128, 3, strides=2, padding="same")(x)
+ x = layers.BatchNormalization()(x)
+ x = layers.Activation("relu")(x)
+
+ previous_block_activation = x # Set aside residual
+
+ for size in [256, 512, 728]:
+ x = layers.Activation("relu")(x)
+ x = layers.SeparableConv2D(size, 3, padding="same")(x)
+ x = layers.BatchNormalization()(x)
+
+ x = layers.Activation("relu")(x)
+ x = layers.SeparableConv2D(size, 3, padding="same")(x)
+ x = layers.BatchNormalization()(x)
+
+ x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
+
+ # Project residual
+ residual = layers.Conv2D(size, 1, strides=2, padding="same")(
+ previous_block_activation
+ )
+ x = layers.add([x, residual]) # Add back residual
+ previous_block_activation = x # Set aside next residual
+
+ x = layers.SeparableConv2D(1024, 3, padding="same")(x)
+ x = layers.BatchNormalization()(x)
+ x = layers.Activation("relu")(x)
+
+ x = layers.GlobalAveragePooling2D()(x)
+ if num_classes == 2:
+ activation = "sigmoid"
+ units = 1
+ else:
+ activation = "softmax"
+ units = num_classes
+
+ x = layers.Dropout(0.5)(x)
+ outputs = layers.Dense(units, activation=activation)(x)
+ return keras.Model(inputs, outputs)
+ model = make_model(input_shape=image_size + (1,), num_classes=10)
\ No newline at end of file
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