sample-model2.py

#!/usr/bin/env python

# Copyright 2016-2018 The RamFuzz contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A sample Keras model trainable on the output of ./gencorp.py.  It tries to
predict the test success or failure based on the logged RamFuzz values during
the test run.  It consists of N dense layers in parallel whose outputs are
multiplied.  This is interesting because we know how to translate a fully
trained network like this into a feedback mechanism to the RamFuzz generator --
see ./solver.py.

Unfortunately, this model can currently only reach ~56% accuracy.

Usage: $0 [epochs] [batch_size] [N]
Defaults: epochs=1, batch_size=50, N=50
Expects a train/ subdirectory containing the output of ./gencorp.py.

"""

from keras.constraints import min_max_norm
from keras.layers import BatchNormalization, Dense, Dropout, Embedding, Flatten
from keras.layers import Input
from keras.layers.merge import concatenate, multiply
from keras.metrics import mse
from keras.models import Model
from keras.optimizers import Adam
import glob
import keras.backend as K
import os.path
import rfutils
import sys

gl = glob.glob(os.path.join('train', '*.[sf]'))
poscount, locidx = rfutils.count_locpos(gl)

embedding_dim = 4
dropout_prob = 0.4
dense_count = int(sys.argv[3]) if len(sys.argv) > 3 else 50
optr = Adam(lr=0.03)

K.set_floatx('float64')

in_vals = Input((poscount, 1), name='vals', dtype='float64')
normd = BatchNormalization(
    axis=1, gamma_constraint=min_max_norm(),
    beta_constraint=min_max_norm())(in_vals)
in_locs = Input((poscount, ), name='locs', dtype='uint64')
embed_locs = Embedding(
    locidx.watermark, embedding_dim, input_length=poscount)(in_locs)
merged = concatenate([embed_locs, normd])
dense_list = []
for i in range(dense_count):
    dense_list.append(
        Dropout(dropout_prob)(Dense(1, activation='sigmoid')(Flatten()(
            merged))))
mult = multiply(dense_list)
ml = Model(inputs=[in_locs, in_vals], outputs=mult)
ml.compile(optr, metrics=['acc'], loss=mse)

locs, vals, labels = rfutils.read_data(gl, poscount, locidx)


def fit(
        eps=int(sys.argv[1]) if len(sys.argv) > 1 else 1,
        # Large batches tend to cause NaNs in batch normalization.
        bsz=int(sys.argv[2]) if len(sys.argv) > 2 else 50):
    ml.fit([locs, vals], labels, batch_size=bsz, epochs=eps)


fit()