submit = SubmitField('submit')
app = Flask(__name__)
app.config['SECRET_KEY'] = 'you-will-never-guess'
# ... add more variables here as needed
@app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
def index():
form = InputForm()
if form.validate_on_submit():
print("receive")
print(form.poeminput.data)
a = gan.generate_poem(count=8,
temperature=1.00,
seed=form.poeminput.data)
print(a)
flash('Output of seed \'{}\': \n {} '.format(form.poeminput.data, a))
# print(form.username.data,form.remember_me.data)
return redirect('/index')
return render_template('index.html', title='Poem Generate', form=form)
if __name__ == '__main__':
# server = pywsgi.WSGIServer(('0.0.0.0',80), app)
# server.serve_forever()
gan = GAN()
gan.build()
app.run(debug='True')
def inference(FLAG):
FLAG_save_dir = FLAG.save_dir
FLAG_plot_dir = FLAG.plot_dir
FLAG_batch_size = 32
FLAG_n_dim = 100
gan = GAN()
gan.build(n_dim=FLAG_n_dim, shape=(64, 64, 3))
def initialize_uninitialized(sess):
global_vars = tf.global_variables()
is_not_initialized = sess.run(
[tf.is_variable_initialized(var) for var in global_vars])
not_initialized_vars = [
v for (v, f) in zip(global_vars, is_not_initialized) if not f
]
if len(not_initialized_vars):
sess.run(tf.variables_initializer(not_initialized_vars))
def res_plot(samples, n_row, n_col):
fig = plt.figure(figsize=(n_col * 2, n_row * 2))
gs = gridspec.GridSpec(n_row, n_col)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(sample.reshape(64, 64, 3))
return fig
with tf.Session() as sess:
if FLAG_save_dir is not None:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(FLAG_save_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored %s" % ckpt.model_checkpoint_path)
sess.run(tf.global_variables())
else:
print("No model checkpoint in %s" % FLAG_save_dir)
else:
sess.run(tf.global_variables_initializer())
sess.run(tf.global_variables())
print("Initialized")
print("Plot saved in %s" % FLAG_plot_dir)
# hyper parameters
batch_size = FLAG_batch_size
# re-initialize
initialize_uninitialized(sess)
# plot
np.random.seed(296)
Xplot = sess.run(gan.G_image,
feed_dict={
gan.random_sample:
np.random.uniform(-1, 1,
[batch_size, gan.n_dim]).astype(
np.float32),
gan.is_train:
False
})
fig = res_plot(Xplot, int(batch_size / 8), 8)
plt.savefig(os.path.join(FLAG_plot_dir, 'fig2_3.jpg'),
bbox_inches='tight')
plt.close(fig)
tb = program.TensorBoard()
tb.configure(argv=[None, '--logdir', utils.LOG_DIR])
url = tb.launch()
print(url)
# initial scanpy train & test splits
data = sc.read_h5ad('data/GSE144136_preprocessed.h5ad')
train = sc.pp.subsample(data=data, fraction=0.90, copy=True, random_state=utils.RANDOM)
test = sc.pp.subsample(data=data, fraction=0.10, copy=True, random_state=utils.RANDOM)
# build model
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = GAN(gex_size=train.shape[1], num_cells_generate=test.shape[0])
model.compile()
model.build(input_shape=(model.hyperparams.batch_size, model.hyperparams.latent_dim)) # req. for subclassed models
# process data for training
train_tf = tf.data.Dataset.from_tensor_slices(train.X). \
cache(). \
shuffle(buffer_size=train.shape[0], seed=utils.RANDOM). \
batch(batch_size=model.hyperparams.batch_size * strategy.num_replicas_in_sync, num_parallel_calls=tf.data.AUTOTUNE). \
prefetch(buffer_size=tf.data.AUTOTUNE)
train_tf_distributed = strategy.experimental_distribute_dataset(train_tf)
test_tf = tf.data.Dataset.from_tensor_slices(test.X). \
cache(). \
shuffle(buffer_size=test.shape[0], seed=utils.RANDOM). \
prefetch(buffer_size=tf.data.AUTOTUNE)
tb_callback = tf.keras.callbacks.TensorBoard(log_dir=utils.LOG_DIR + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
