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diff --git a/src/cnn_train.py b/src/cnn_train.py
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+# train5.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
+
+# Custom activation function
+from tensorflow import constant as k
+from tensorflow import clip_by_value
+from tensorflow.math import add, divide, multiply, square
+
+# Computing size
+from tensorflow.keras.backend import get_value
+
+# Building models
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Concatenate
+from tensorflow.keras.layers import Input
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Convolution2D
+from tensorflow.keras.layers import Flatten
+
+
+# We need something that's close to logistic, but cheaper to compute.
+# (12.0+x+50.0*x/(x*x+10.0))/24.0, clipped between 0 and 1
+# as it would otherwise exceed this range at +- 4.6 or so
+def clipped_pade_logistic(x):
+    val = add(k(12.0),
+              add(x, multiply(k(50.0),
+                              divide(x, add(square(x), k(10.0))))))
+    return clip_by_value(divide(val, k(24.0)), 0.0, 1.0)
+
+
+# A cheaper version of tanh, x/6+25*x/(6*(2*x*x+5)),
+# clipped between -1 and 1.
+def clipped_pade_tanh(x):
+    val = add(divide(x, k(6.0)),
+              multiply(k(25.0),
+                       divide(x, multiply(k(6.0), add(k(5.0),
+                                                      multiply(k(2.0), square(x)))))))
+    return clip_by_value(val, -1.0, 1.0)
+
+
+def make_model(size, magic=[12, 9, 9]):
+    # Our model for the stacks, a small CNN
+    stack_shape = (size, size, 6)
+    stack_input = Input(shape=stack_shape)
+    stack_model = Convolution2D(magic[0], kernel_size=(3, 3), strides=(1, 1),
+                                padding='valid', activation="relu",
+                                use_bias=True)(stack_input)
+    stack_model = Flatten()(stack_model)
+    stack_model = Model(inputs=stack_input, outputs=stack_model)
+    # The overall model
+    flats_input = Input(shape=(2,))
+    combn_input = Concatenate()([stack_model.output, flats_input])
+    model = Dense(magic[1], activation="relu", use_bias=True)(combn_input)
+    model = Dense(magic[2], activation="relu", use_bias=True)(model)
+    model = Dense(1, activation=clipped_pade_logistic, use_bias=True)(model)
+    model = Model(inputs=[stack_model.input, flats_input], outputs=model)
+    model.compile(optimizer='adam',
+                  loss='mean_squared_error',
+                  # loss='mean_absolute_error',
+                  # loss='mean_absolute_percentage_error',
+                  metrics=['accuracy'])
+    model.summary()
+    return model
+
+
+def load_data(size):
+    shape = (-1, size, size, 6)
+    # Load training data
+    tr_fn = "training-"+str(size)+".csv"
+    training_csv = pd.read_csv(tr_fn)  #.head(100000)
+    training_data = training_csv
+    training_stack_input = np.array(training_data.iloc[:, 2:-1]).reshape(shape, order='F')
+    training_flats_input = np.array(training_data.iloc[:, 0:2])
+    training_input = [training_stack_input, training_flats_input]
+    training_outcome = training_data.iloc[:, -1:]
+    # Load validation data
+    val_fn = "validation-"+str(size)+".csv"
+    val_data = pd.read_csv(val_fn).tail(20000)
+    val_stack_input = np.array(val_data.iloc[:, 2:-1]).reshape(shape, order='F')
+    val_flats_input = np.array(val_data.iloc[:, 0:2])
+    val_input = [val_stack_input, val_flats_input]
+    val_outcome = val_data.iloc[:, -1:]
+    return [(training_input, training_outcome), (val_input, val_outcome)]
+
+
+def train(size, model, data, iterations=1, epochs=10):
+    (training_input, training_outcome), (val_input, val_outcome) = data
+    results = []
+    for i in range(0, iterations):
+        print("Iteration {0}/{1}".format(i+1, iterations))
+        model.fit(training_input, training_outcome, epochs=epochs,
+                  validation_data=(val_input, val_outcome),
+                  verbose=True)
+        v_loss, v_accuracy = model.evaluate(val_input, val_outcome,
+                                            verbose=False, batch_size=16)
+        t_loss, t_accuracy = model.evaluate(training_input, training_outcome,
+                                            verbose=False, batch_size=32)
+        results += [((v_loss, t_loss), (v_accuracy, t_accuracy))]
+    print("\nScores")
+    for i in range(len(results)):
+        print("Iteration {0}: {1}".format(i+1, results[i]))
+    return results
+
+
+def model_size(model):
+    return sum([np.prod(get_value(w).shape) for w in model.trainable_weights])
+
+
+def magic_search(data):
+    results = []
+    for width in range(9, 18):
+        for dense1 in range(9, 16):
+            for dense2 in range(9, max(32, dense1*2)):
+                m = make_model(5, [width, dense1, dense2])
+                if model_size(m) < 1990:
+                    results += [([width, dense1, dense2],
+                                 train(5, m, data, iterations=1, epochs=20))]
+    print("\nSummary")
+    for r in results:
+        print("Parameters {0}: {1}".format(r[0], r[1][0]))
+    return results
+
+
+def write_weights(model):
+    def fix(val):
+        string = str(np.array(val).tolist())
+        string = string.replace("[", "{").replace("]", "}")
+        return string
+    # Prepare everything in a sane memory order This isn't exactly in
+    # the correct order that tensorflow uses, because memory access
+    # out of order is an eyesore. Compared to tensorflow, the C
+    # implementation has the board reflected about the diagonal.
+    conv2d_weights = transpose(model.trainable_variables[0], perm=[3, 0, 1, 2])
+    conv2d_biases = model.trainable_variables[1]
+    dense1_weights = transpose(model.trainable_variables[2], perm=[1, 0])
+    dense1_biases = model.trainable_variables[3]
+    dense2_weights = transpose(model.trainable_variables[4], perm=[1, 0])
+    dense2_biases = model.trainable_variables[5]
+    output_weights = transpose(model.trainable_variables[6], perm=[1, 0])[0]
+    output_bias = model.trainable_variables[7][0]
+    # Prepare formatting
+    names = ["conv2d_weights[KERN_NUM][KERN_SIZE][KERN_SIZE][KERN_CHAN]",
+             "conv2d_biases[KERN_NUM]",
+             "dense1_weights[DENSE1_NUM][CONV_NUM+2]",
+             "dense1_biases[DENSE1_NUM]",
+             "dense2_weights[DENSE2_NUM][DENSE1_NUM]",
+             "dense2_biases[DENSE2_NUM]",
+             "output_weights[DENSE2_NUM]",
+             "output_bias"]
+    variables = [conv2d_weights, conv2d_biases,
+                 dense1_weights, dense1_biases,
+                 dense2_weights, dense2_biases,
+                 output_weights, output_bias]
+    # Write to file
+    f = open("weights.txt", "w")
+    f.write("#include \"weights.h\"\n\n")
+    for (name, val) in zip(names, variables):
+        f.write("const float "+name+" =\n"+fix(val)+";\n\n")
+    f.close()
+
+
+data = load_data(5)
+model = make_model(5, [12, 11, 8])
+results = train(5, model, data, iterations=5, epochs=10)
+write_weights(model)
+
+# results = magic_search(data)