diff options
Diffstat (limited to 'src/nn_train.py')
| -rw-r--r-- | src/nn_train.py | 31 |
1 files changed, 16 insertions, 15 deletions
diff --git a/src/nn_train.py b/src/nn_train.py index f2550b4..03852f5 100644 --- a/src/nn_train.py +++ b/src/nn_train.py @@ -56,7 +56,7 @@ def train(size, model, data, iterations=1, epochs=10, batch=None): tra_res = model.evaluate(tra_input, tra_outcome, verbose=False) results.append((tra_res, val_res)) print(val_res) - write_weights(model, str(i+1).zfill(len(str(iterations))), (tra_res, val_res)) + write_weights(size, model, str(i+1).zfill(len(str(iterations))), (tra_res, val_res)) print("\nScores") for i, data in enumerate(results): print(f"Iteration {i+1}: {data}") @@ -74,33 +74,34 @@ def make_model(size, magic): return model -def write_weights(model, iteration, performance): +def write_weights(size, 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] + size_str = "five" if size==5 else "six" + dense_weights = transpose(model.trainable_variables[0], perm=[1, 0]) + dense_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)] + to_output = [(f"{size_str}_dense_weights[{size_str.upper()}_DENSE_NUM][{size_str.upper()}_INP_NUM]", dense_weights), + (f"{size_str}_dense_biases[{size_str.upper()}_DENSE_NUM]", dense_biases), + (f"{size_str}_output_weights[2][{size_str.upper()}_DENSE_NUM]", output_weights), + (f"{size_str}_output_bias[2]", output_bias)] # Write to file - f = open("weights-"+iteration+".txt", "w") + f = open(f"{size_str}_weights-"+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") + f.write(f"#include \"weights_{size}.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=5, epochs=10, batch=128) +for size in (5,6): + data = load_data(size) + model = make_model(size, 64) + print("Before training", model.evaluate(data[1][0], data[1][1], verbose=False, batch_size=16)) + results = train(size, model, data, iterations=5, epochs=10, batch=128) |