From 67d830ca09328516632cfdb729dce7b20c941481 Mon Sep 17 00:00:00 2001
From: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
Date: Thu, 11 Nov 2021 18:42:23 +0100
Subject: [PATCH] feat(Models): added simple Feed forward layer

---
 model_ff.py   | 96 +++++++++++++++++++++++++++++++++++++++++++++++++++
 model_lstm.py |  6 ++--
 2 files changed, 99 insertions(+), 3 deletions(-)
 create mode 100644 model_ff.py

diff --git a/model_ff.py b/model_ff.py
new file mode 100644
index 0000000..d6794b3
--- /dev/null
+++ b/model_ff.py
@@ -0,0 +1,96 @@
+#%% Import all the stuff, load data, define constants
+from load_data import load_files
+import pandas as pd
+from tensorflow import keras
+from utils.normalize import normalize
+import tensorflow as tf
+from utils.visualize import visualize_loss
+
+data = load_files('data/', False)
+data.reset_index(drop=True, inplace=True)
+data = data[[column for column in data.columns if not column.endswith('volume')]]
+# data = data[["ETH_returns", "BTC_returns"]]
+
+ticker_to_predict = 'ETH_returns'
+
+learning_rate = 0.002
+batch_size = 64
+epochs = 100
+
+split_fraction = 0.715
+train_split = int(split_fraction * int(data.shape[0]))
+
+past = 10
+future = 1
+
+start = past + future
+end = start + train_split
+
+#%% split data into training - validation sets
+train_data = data.loc[0 : train_split - 1]
+val_data = data.loc[train_split:]
+
+#%% create features and target for training set & keras dataset
+
+x_train = normalize(train_data).values
+# x_train = normalize(train_data).drop(ticker_to_predict, axis=1).values
+y_train = normalize(data).iloc[start:end][ticker_to_predict].values
+
+dataset_train = keras.preprocessing.timeseries_dataset_from_array(
+    x_train,
+    y_train,
+    sequence_length=past,
+    batch_size=batch_size,
+)
+
+
+#%% create features and target for validation set & keras dataset
+x_end = len(val_data) - past - future
+label_start = train_split + past + future
+
+x_val = normalize(val_data).iloc[:x_end].values
+# x_val = normalize(val_data).iloc[:x_end].drop(ticker_to_predict, axis=1).values
+y_val = normalize(data).iloc[label_start:][ticker_to_predict].values
+
+dataset_val = keras.utils.timeseries_dataset_from_array(
+    x_val,
+    y_val,
+    sequence_length=past,
+    batch_size=batch_size,
+)
+
+
+#%%
+
+for batch in dataset_train.take(10):
+    batch_inputs, batch_targets = batch
+
+print("Input shape:", batch_inputs.shape)
+print("Target shape:", batch_targets.shape)
+
+print(batch_inputs)
+print(batch_targets)
+
+# %%
+model = keras.Sequential()
+model.add(keras.layers.Dense(units = 10, activation = 'sigmoid', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 4, activation = 'sigmoid'))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 1))
+
+optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm=1.0)
+model.compile(optimizer=optimizer, loss="mean_squared_error")
+model.summary()
+
+# %%
+path_checkpoint = "model_checkpoint.h5"
+
+history = model.fit(
+    dataset_train,
+    epochs=epochs,
+    validation_data=dataset_val,
+)
+# %%
+
+visualize_loss(history, "Training and Validation Loss")
\ No newline at end of file
diff --git a/model_lstm.py b/model_lstm.py
index acdd8c0..f17e385 100644
--- a/model_lstm.py
+++ b/model_lstm.py
@@ -74,10 +74,10 @@ print(batch_targets)
 # %%
 model = keras.Sequential()
 model.add(keras.layers.LSTM(units = 32, return_sequences = True, activation = 'relu', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
-# model.add(keras.layers.Dropout(0.4))
-# model.add(keras.layers.Dense(units = 10, activation = 'relu'))
 model.add(keras.layers.Dropout(0.4))
-model.add(keras.layers.Dense(units = 1, activation = 'relu'))
+model.add(keras.layers.Dense(units = 10, activation = 'relu'))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 1))
 
 optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm=1.0)
 model.compile(optimizer=optimizer, loss="mean_squared_error")