batch_size=batch_size)
# In[5]:
cnn_params = {
'out_dims': [256, 256, 256, 128],
'kernel_sizes': 64,
'pool_sizes': 1
}
c = cnn.model(seq_len=seq_len, input_op=input_op, **cnn_params)
#a = tf.transpose(c.output, perm=[0, 2, 1])
#a = tf.nn.top_k(a, k=8, sorted=False, name='MAX_POOL').values
#a = tf.transpose(a, perm=[0, 2, 1])
a = tf.reduce_mean(c.output, axis=1)
fc = classifier.model(input_op=a, fc_sizes=[])
logits = fc.logits
pred = fc.pred
MODEL_PATH = '/tmp/balanced/' + c.name + fc.name
MODEL_EXISTS = os.path.exists(MODEL_PATH)
if MODEL_EXISTS:
print('Model directory is not empty, removing old files')
shutil.rmtree(MODEL_PATH)
# In[6]:
def measure_time(op, feed_dict={}, n_times=10):
with tf.Session() as sess:
residual_input = tf.contrib.layers.max_pool2d(
residual_input,
kernel_size=[RESIDUAL_POOL, 1],
stride=[RESIDUAL_POOL, 1])
c = cnn.model(seq_len=seq_len,
input_op=residual_input,
residual=True,
keep_prob=keep_prob,
model_name='CNN_block_%d' % i,
**cnn_params)
residual_input += c.output
res_out = tf.squeeze(residual_input, axis=2)
a = tf.reduce_mean(res_out, axis=1)
fc = classifier.model(input_op=a, fc_sizes=[16], keep_prob=keep_prob)
pred = fc.pred
# ### Run predictor
label_dict = {0: 'N', 1: 'A', 2: 'O', 3: '~'}
saver = tf.train.Saver()
with tf.Session() as sess:
print('Sess started')
coord = tf.train.Coordinator()
saver.restore(sess, 'model/pool4--cnn32x64-64x64-64x64--fc16-20000')
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print('Evaluating')
output = sess.run(pred,
# batch_size=batch_size, path='./data/train.TFRecord')
input_op = tf.placeholder(tf.float32, [1, None])
seq_len = tf.placeholder(tf.float32, [1])
c = cnn.model(seq_len=seq_len, input_op=input_op, **cnn_params)
r = rnn.get_model(batch_size=batch_size,
seq_len=seq_len,
input_op=c.output,
**rnn_params)
f = fourier.get_output(seq_len=seq_len, input_op=input_op, **fourier_params)
td = time_domain.get_output(seq_len=seq_len,
input_op=input_op,
**time_domain_params)
concatenated_features = tf.concat([r.last_output, f, td], 1)
fc = classifier.model(input_op=concatenated_features, **fc_params)
logits = fc.logits
pred = fc.pred
print('Building model... done!')
# Load recording
print('Loading record...', end=' ')
# dir = "./validation/"
assert len(sys.argv) == 2, "Wrong parameter list in the call of that script."
fname = sys.argv[1]
assert os.path.isfile(fname + ".mat"), "Not existing file: " + fname + ".mat"
data = io.loadmat(fname + '.mat')['val'].astype(np.float32).squeeze()
data -= data.mean()
data /= data.std()
print('done!')
batch_size=batch_size, path='./data/VALIDATION.TFRecord')
validation_feed_dict = {
input_op: val_input_op,
seq_len: val_seq_len,
label: val_label,
batch_size: 128
}
# In[5]:
cnn_params = {'out_dims': [128, 256, 256], 'kernel_sizes': 64, 'pool_sizes': 1}
c = cnn.model(seq_len=seq_len, input_op=input_op, **cnn_params)
a = tf.reduce_mean(c.output, axis=1)
fc = classifier.model(input_op=a, fc_sizes=[256, 256])
logits = fc.logits
pred = fc.pred
MODEL_PATH = '/tmp/balanced/' + c.name + fc.name
MODEL_EXISTS = os.path.exists(MODEL_PATH)
if MODEL_EXISTS:
print('Model directory is not empty, removing old files')
shutil.rmtree(MODEL_PATH)
# In[6]:
def measure_time(op, feed_dict={}, n_times=10):
with tf.Session() as sess:
