Magic

1 files changed, 24 insertions, 29 deletions

diff --git a/src/train5.py b/src/train5.py
index ea25e5e..dd02df7 100644
--- a/src/train5.py
+++ b/src/train5.py
@@ -1,9 +1,6 @@
 import pandas as pd
 import numpy as np
 
-from tensorflow.keras.regularizers import l1
-from tensorflow.keras.regularizers import l2
-
 import tensorflow.keras.losses
 
 from tensorflow.keras.models import Sequential
@@ -11,55 +8,53 @@ from tensorflow.keras.layers import Dropout
 from tensorflow.keras.layers import Input
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.layers import Convolution2D
-from tensorflow.keras.layers import MaxPooling2D
 from tensorflow.keras.layers import Flatten
-from tensorflow.keras.layers import Activation
-from tensorflow.keras.layers import BatchNormalization
 
 # 30 10(11) 5
 # 27 17 4
 
-shape = (-1, 5, 5, 8)
+size = 6
+magic = int(size*3/2.0)
+shape = (-1, size, size, 8)
 
 model = Sequential([
     Input(shape[1:]),
-    Convolution2D(1, kernel_size=3, strides=(1, 1),
-                  padding='same',
+    Convolution2D(magic, kernel_size=(3, 3), strides=(1, 1),
+                  padding='valid',
                   activation="relu", use_bias=True),
-  # Dropout(0.2),
     Flatten(),
-    Dense(10, activation="relu", use_bias=True),
-  # Dropout(0.2),
-  # Dense(10, activation="relu", use_bias=False),
-  # Dropout(0.2),
+    Dropout(0.2),
+    Dense(magic, activation="relu", use_bias=True),
     Dense(2, activation="softmax")
 ])
 
-        # bias_regularizer=l2(0.001),
-        # kernel_regularizer=l1(0.001)),
-
 model.compile(loss='categorical_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])
 
 model.summary()
 
-csv = pd.read_csv("training-5.csv")
-
-training_data = csv.head(100000).copy()
+tr_fn = "validation-"+str(size)+".csv"
+training_csv = pd.read_csv(tr_fn).head(80000)
+training_data = training_csv
 training_input = np.array(training_data.iloc[:, :-2]).reshape(shape)
 training_outcome = training_data.iloc[:, -2:]
 
-test_data = csv.tail(20000).copy()
-test_input = np.array(test_data.iloc[:, :-2]).reshape(shape)
-test_outcome = test_data.iloc[:, -2:]
+val_fn = "validation-"+str(size)+".csv"
+val_data = pd.read_csv(val_fn).tail(20000)
+val_input = np.array(val_data.iloc[:, :-2]).reshape(shape)
+val_outcome = val_data.iloc[:, -2:]
 
 results = []
-for i in range(0, 1):
+reps = 10
+for i in range(0, reps):
     print("Iteration ", i)
-    history = model.fit(training_input, training_outcome, epochs=10,
-                        validation_data=(test_input, test_outcome))
-    loss, accuracy = model.evaluate(test_input, test_outcome)
-    results += [(i, loss, accuracy)]
+    history = model.fit(training_input, training_outcome, epochs=1,
+                        validation_data=(val_input, val_outcome),
+                        verbose=False)
+    loss, accuracy = model.evaluate(val_input, val_outcome, verbose=False)
+    results += [(loss, accuracy)]
 
-print("\n\nResults: ", results)
+print("\n\n")
+for i in range(0, reps):
+    print("Iteration ", i, ": ", results[i])