1 files changed, 106 insertions, 0 deletions
diff --git a/src/nn_train.py b/src/nn_train.py
new file mode 100644
index 0000000..12ce868
--- /dev/null
+++ b/src/nn_train.py
@@ -0,0 +1,106 @@
+# cnn_train.py, train a small CNN to recognise winning Tak positions
+#
+# Copyright (C) 2021, tslil clingman
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+
+# Loading data
+import pandas as pd
+import numpy as np
+
+# Output of weights
+from tensorflow import transpose
+
+# Building models
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense
+
+
+def load_data(size):
+    output_len = 2
+    # Load training data
+    tr_fn = "data/training-"+str(size)+".csv"
+    training_csv = pd.read_csv(tr_fn)
+    training_data = training_csv
+    training_input = np.array(training_data.iloc[:, 0:-output_len])
+    training_outcome = np.array(training_data.iloc[:, -output_len:])
+    # Load validation data
+    val_fn = "data/validation-"+str(size)+".csv"
+    val_data = pd.read_csv(val_fn)
+    val_input = np.array(val_data.iloc[:, 0:-output_len])
+    val_outcome = np.array(val_data.iloc[:, -output_len:])
+    return ((training_input, training_outcome), (val_input, val_outcome))
+
+
+def train(size, model, data, iterations=1, epochs=10, batch=None):
+    (tra_input, tra_outcome), (val_input, val_outcome) = data
+    results = []
+    for i in range(0, iterations):
+        print("Iteration {0}/{1}".format(i+1, iterations))
+        model.fit(tra_input, tra_outcome, epochs=epochs,
+                  validation_data=(val_input, val_outcome),
+                  verbose=True, batch_size=batch)
+        val_res = model.evaluate(val_input, val_outcome, verbose=False)
+        tra_res = model.evaluate(tra_input, tra_outcome, verbose=False)
+        results.append((tra_res, val_res))
+        print(val_res)
+        write_weights(model, i+1, (tra_res, val_res))
+    print("\nScores")
+    for i, data in enumerate(results):
+        print(f"Iteration {i}: {data}")
+    return results
+
+
+def make_model(size, magic):
+    inputs = Input(shape=(size * size + 3,))
+    model = inputs
+    model = Dense(magic, activation="relu", use_bias=True)(model)
+    model = Dense(2, activation="relu", use_bias=True)(model)
+    model = Model(inputs=inputs, outputs=model)
+    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+    model.summary()
+    return model
+
+
+def write_weights(model, iteration, performance):
+    def fix(val):
+        string = str(np.array(val).tolist())
+        string = string.replace("[", "{").replace("]", "}")
+        return string
+    dense1_weights = transpose(model.trainable_variables[0], perm=[1, 0])
+    dense1_biases = model.trainable_variables[1]
+    output_weights = transpose(model.trainable_variables[2], perm=[1, 0])
+    output_bias = model.trainable_variables[3]
+    # Prepare output
+    to_output = [("dense1_weights[DENSE_NUM][INP_NUM]",dense1_weights)
+                 ("dense1_biases[DENSE_NUM]", dense1_biases),
+                 ("output_weights[2][DENSE_NUM]", output_weights),
+                 ("output_bias[2]", output_bias)]
+    # Write to file
+    f = open("weights-"+str(iteration)+".txt", "w")
+    f.write("/*\n")
+    model.summary(print_fn=lambda l: f.write(" * "+l+"\n"))
+    f.write(" * "+str(performance)+"\n*/\n\n")
+    f.write("#include \"weights.h\"\n\n")
+    for (name, val) in to_output:
+        f.write("const float "+name+" =\n"+fix(val)+";\n\n")
+    f.close()
+
+
+data = load_data(5)
+model = make_model(5, 64)
+
+print("Before training", model.evaluate(data[1][0], data[1][1], verbose=False, batch_size=16))
+results = train(5, model, data, iterations=1, epochs=10, batch=None)