小编Val*_*io 的帖子

def train():
# Model
model = Model()

# Loss, Optimizer
global_step = tf.Variable(1, dtype=tf.int32, trainable=False, name='global_step')
loss_fn = model.loss()
optimizer = tf.train.AdamOptimizer(learning_rate=TrainConfig.LR).minimize(loss_fn, global_step=global_step)

# Summaries
summary_op = summaries(model, loss_fn)

with tf.Session(config=TrainConfig.session_conf) as sess:

    # Initialized, Load state
    sess.run(tf.global_variables_initializer())
    model.load_state(sess, TrainConfig.CKPT_PATH)

    writer = tf.summary.FileWriter(TrainConfig.GRAPH_PATH, sess.graph)

    # Input source
    data = Data(TrainConfig.DATA_PATH)

    loss = Diff()
    for step in xrange(global_step.eval(), TrainConfig.FINAL_STEP):

            mixed_wav, src1_wav, src2_wav, _ = data.next_wavs(TrainConfig.SECONDS, TrainConfig.NUM_WAVFILE, step)

            mixed_spec = to_spectrogram(mixed_wav)
            mixed_mag = get_magnitude(mixed_spec)

            src1_spec, src2_spec = to_spectrogram(src1_wav), to_spectrogram(src2_wav)
            src1_mag, src2_mag = …