add documentation for CNN, allow it to interact with model_training_parameters

docs/freqai-configuration.md

@@ -242,3 +242,23 @@ If you want to predict multiple targets you must specify all labels in the same
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
 ```
+
+### Convolutional Neural Network model
+
+The `CNNPredictionModel` is a non-linear regression based on `Tensorflow` which follows very similar configuration to the other regressors. Feature engineering and label creation remains the same as highlighted [here](#building-a-freqai-strategy) and [here](#setting-model-targets). Control of the model is focused in the `model_training_parameters` configuration dictionary, which accepts any hyperparameter available to the CNN `fit()` function of Tensorflow [more here](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit). For example, this is where the `epochs` and `batch_size` are controlled:
+
+```json
+    "model_training_parameters" : {
+        "batch_size": 64,
+        "epochs": 10,
+        "verbose": "auto",
+        "shuffle": false
+        "workers": 1,
+        "use_multiprocessing": False
+    }
+```
+
+Running the `CNNPredictionModel` is the same as other regressors: `--freqaimodel CNNPredictionModel`.
+
+
+```

freqtrade/freqai/prediction_models/CNNPredictionModel.py

@@ -17,8 +17,6 @@ logger = logging.getLogger(__name__)
 # tf.config.run_functions_eagerly(True)
 # tf.data.experimental.enable_debug_mode()
 
-MAX_EPOCHS = 10
-
 
 class CNNPredictionModel(BaseTensorFlowModel):
     """
@@ -46,7 +44,12 @@ class CNNPredictionModel(BaseTensorFlowModel):
             )
 
         n_features = len(data_dictionary["train_features"].columns)
-        BATCH_SIZE = self.freqai_info.get("batch_size", 64)
+        BATCH_SIZE = self.model_training_parameters.get("batch_size", 64)
+
+        # we need to remove batch_size from the model_training_params because
+        # we dont want fit() to get the incorrect assignment (we use the WindowGenerator)
+        # to handle our batches.
+        self.model_training_parameters.pop('batch_size')
         input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
 
         w1 = WindowGenerator(
@@ -84,8 +87,7 @@ class CNNPredictionModel(BaseTensorFlowModel):
 
         model.fit(
             w1.train,
-            epochs=MAX_EPOCHS,
-            shuffle=False,
+            **self.model_training_parameters,
             validation_data=val_data,
             callbacks=[early_stopping],
             verbose=1,