init

StockTrainer.py

@@ -1,6 +1,8 @@
 import os
 import keras
 import numpy as np
+from numpy import zeros, newaxis
+import tensorflow as tf
 from stock.util.Stock2Vector import Stock2Vector
 from classification_models.keras import Classifiers
 
@@ -15,6 +17,7 @@ class StockTrainer:
         return
 
     def getDataset(self, stock_code):
+        VECTOR_SIZE = 299
         df, minmax_df = self.stock2Vector.makeTrainData(stock_code)
 
         TOTAL_X, TOTAL_Y = [], []
@@ -26,17 +29,26 @@ class StockTrainer:
             else:
                 TOTAL_X.append(df[key].tolist())
 
+        SIZE_WIDTH = len(TOTAL_X[0])
+        SIZE_HEIGHT = len(TOTAL_X)
         X, Y = [], []
-        for i in range(299, len(TOTAL_X[0])):
+        for i in range(VECTOR_SIZE, SIZE_WIDTH):
-            temp_X, temp_Y = np.zeros((299, 299)), np.zeros(0)
+            temp_X, temp_Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE)), np.zeros(0)
-            idx = 0
+            for j in range(SIZE_HEIGHT):
-            for j in range(i-299, i):
+                temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i-VECTOR_SIZE:i]
-                for k in range(len(TOTAL_X)):
+            temp_X = np.stack([temp_X, temp_X, temp_X], axis=-1)
-                    temp_X[k][idx] = TOTAL_X[k][j]
-                idx += 1
             X.append(temp_X)
-            Y.append(TOTAL_Y[0][i])
+            if int(TOTAL_Y[0][i]) == 0:
+                Y.append([1, 0, 0])
+            elif int(TOTAL_Y[0][i]) == 0.5:
+                Y.append([0, 1, 0])
+            else:
+                Y.append([0, 0, 1])
+            if i >= VECTOR_SIZE+10:
+                break
 
+        X = np.asarray(X)
+        Y = np.asarray(Y)
         return X, Y
 
     def train(self, stock_code):
@@ -49,14 +61,22 @@ class StockTrainer:
         n_classes = 3
 
         # build model
-        base_model = ResNet18(input_shape=(299, 299, 3), weights='imagenet', include_top=False)
+        base_model = ResNet18(input_shape=(299, 299, 3), include_top=False)
-        x = keras.layers.GlobalAveragePooling2D()(base_model.output)
+        x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
-        output = keras.layers.Dense(n_classes, activation='softmax')(x)
+        output = tf.keras.layers.Dense(n_classes, activation='softmax')(x)
         model = keras.models.Model(inputs=[base_model.input], outputs=[output])
 
         # train
         model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
-        model.fit(X, Y)
+
+        checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
+        chekpoint = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filename, save_weights_only=True, verbose=1)
+        earlystop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3, mode="auto")
+
+        model.fit(x=X,
+                  y=Y,
+                  epochs=10,
+                  callbacks=[chekpoint, earlystop])
 
         return
 
@@ -65,7 +85,6 @@ if __name__ == "__main__":
     PROJECT_HOME = os.path.join(os.path.dirname(__file__))
     RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
 
-    # to check bying
     stock_codes = {
         # 252670
         # 122630