def R2N2_advanced_model(image_placeholder, label_placeholder, \
mini_batch_size, H,W,C, NX, NY, NZ, T, learning_rate = 5e-4):
'''
:param mini_batch_size:
:param H:
:param W:
:param C:
:param NX:
:param NY:
:param NZ:
:param T:
:param learning_rate:
:return:
'''
# # sequence_placeholder = tf.placeholder(tf.float32, shape = [batch_size,T,H,W,C])
# image_placeholder = tf.placeholder(tf.float32, shape=[mini_batch_size, H, W, C])
# # need the 2 because of one-hot encoding for softmax
# label_placeholder = tf.placeholder(tf.float32, shape=[mini_batch_size, NX, NY, NZ, 2]);
## specify total graph
sequence = [image_placeholder for i in range(T)]
encoded_sequence = list()
for image in sequence:
encoded_out = encoder.encoder_resnet(image)
encoded_sequence.append(encoded_out)
# convert encoded sequence, which is a list of tensors
# to a tensor of tensors
encoded_sequence = tf.stack(encoded_sequence)
encoded_sequence = tf.transpose(encoded_sequence, perm=[1, 0, 2])
## after we use the encoder, we should have a sequence of dense outputs
conv_lstm_hidden = my_3d_lstm.my_3d_lstm(encoded_sequence)
##decode the lstm output, which is just the hidden state, 3D
# pass it through the decoder
n_deconvfilter = [128, 128, 128, 64, 32, 2]
logits = decoder.decoder_res_upsample(conv_lstm_hidden, n_deconvfilter)
# squeeze logits so it is 4 dimensional
logits = tf.squeeze(logits)
# check shape
outputs = tf.contrib.layers.softmax(logits)
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=label_placeholder,
logits=logits,
)
loss = tf.layers.flatten(loss)
loss = tf.reduce_mean(loss)
## define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
optimizer = optimizer.minimize(loss)
#
predictions = tf.argmax(outputs, axis=4)
return loss, optimizer, predictions
print('total parameters:', total_parameters)
#check type of encoded sequence
print('type of encoded seq:', type(encoded_sequence))
print('seq[0] shape:', encoded_sequence[0].shape)
#convert encoded sequence, which is a list of tensors
#to a tensor of tensors
encoded_sequence = tf.stack(encoded_sequence)
encoded_sequence = tf.transpose(encoded_sequence, perm = [1,0,2])
print('encoded seq shape:', encoded_sequence.shape)
## after we use the encoder, we should have a sequence of dense outputs
#conv_lstm_hidden = simple_lstm.simple_lstm(encoded_sequence);
#conv_lstm_hidden = my_3d_gru.my_3d_gru(encoded_sequence, cube_size=4, num_channels=conv_lstm_hidden_channels, filter_size=3)
conv_lstm_hidden = my_3d_lstm.my_3d_lstm(encoded_sequence, cube_size=4, num_channels=conv_lstm_hidden_channels, filter_size=3)
print('lstm hidden shape:', conv_lstm_hidden.shape)
##decode the lstm output, which is just the hidden state, 3D
# pass it through the decoder
n_deconvfilter = [64, 32, 16, 16, 16, 1]
logits = decoder.decoder_simple(conv_lstm_hidden, n_deconvfilter)
#squeeze logits so it is 4 dimensional
logits = tf.squeeze(logits)
outputs = tf.nn.sigmoid(logits)
outputs = tf.identity(outputs, name='classification_activation')
#check shape