Create Cnn.md

Cnn.md

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+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization
+
+# -------------------------
+# Construction du modèle CNN
+# -------------------------
+def build_cnn_model(input_shape, num_classes, filter1, filter2, filter3, learning_rate, dropout):
+    model = Sequential()
+
+    # 1er bloc convolution
+    model.add(Conv1D(filters=filter1, kernel_size=3, activation='relu', padding='same', input_shape=input_shape))
+    model.add(BatchNormalization())
+    model.add(MaxPooling1D(pool_size=2))
+
+    # 2e bloc
+    model.add(Conv1D(filters=filter2, kernel_size=3, activation='relu', padding='same'))
+    model.add(BatchNormalization())
+    model.add(MaxPooling1D(pool_size=2))
+
+    # 3e bloc
+    model.add(Conv1D(filters=filter3, kernel_size=3, activation='relu', padding='same'))
+    model.add(BatchNormalization())
+    model.add(MaxPooling1D(pool_size=2))
+
+    # Fully connected
+    model.add(Flatten())
+    model.add(Dense(256, activation='relu'))
+    model.add(Dropout(dropout))
+    model.add(Dense(num_classes, activation='softmax'))
+
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
+        loss='categorical_crossentropy',
+        metrics=['accuracy']
+    )
+
+    return model