training dataset

model training data

training

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+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
+from tensorflow.keras.optimizers import Adam
+
+# Assuming base_model is your pre-loaded Mixtral 8x7b model
+base_model.trainable = False  # Freeze the layers
+
+# Add custom layers
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+x = Dense(1024, activation='relu')(x)
+predictions = Dense(num_classes, activation='softmax')(x)
+
+# This is the model we will train
+model = Model(inputs=base_model.input, outputs=predictions)
+
+# Compile the model
+model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(train_dataset, validation_data=val_dataset, epochs=10)