activation=tf.nn.sigmoid)
n = tf.shape(deconv1)[0]
szx = tf.shape(deconv1)[1]
szy = tf.shape(deconv1)[2]
estimated = tf.slice(deconv1, [0, 4, 4, 0], [n, szx - 8, szy - 8, 1],
"estimated")
# Loss
estimated_resized = tf.reshape(estimated,
[-1, patch_size_label * patch_size_label])
labels_resized = tf.reshape(y, [-1, patch_size_label * patch_size_label])
labels_resized = tf.cast(labels_resized, tf.float32)
loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_resized,
logits=estimated_resized))
# Optimizer
train_op = tf.compat.v1.train.AdamOptimizer(
lr, name="optimizer").minimize(loss)
# Initializer, saver, session
init = tf.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=20)
sess = tf.compat.v1.Session()
sess.run(init)
# Let's export a SavedModel
create_savedmodel(sess, ["x:0", "y:0", "is_training:0"], ["estimated:0"],
params.outdir)
# Placeholders
x = tf.compat.v1.placeholder(tf.float32, [None, None, None, 4], name="x")
y = tf.compat.v1.placeholder(tf.int32, [None, None, None, 1], name="y")
lr = tf.compat.v1.placeholder_with_default(tf.constant(0.0002,
dtype=tf.float32,
shape=[]),
shape=[],
name="lr")
# Output
y_estimated, y_label = my_model(x)
# Loss function
cost = tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(y_estimated, [-1, params.nclasses]))
# Optimizer
optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=lr, name="optimizer").minimize(cost)
# Initializer, saver, session
init = tf.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=20)
sess = tf.compat.v1.Session()
sess.run(init)
# Create a SavedModel
create_savedmodel(sess, ["x:0", "y:0"], ["features:0", "prediction:0"],
params.outdir)
params.n_dims, params.n_timestamps)
testPrediction = tf.argmax(pred_full, 1, name="prediction")
loss_full = tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_full, [-1, params.nclasses]))
loss_c1 = tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_c1, [-1, params.nclasses]))
loss_c2 = tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_c2, [-1, params.nclasses]))
cost = loss_full + (0.3 * loss_c1) + (0.3 * loss_c2)
optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate, name="optimizer").minimize(cost)
correct = tf.equal(tf.argmax(pred_full, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float64))
# Initializer, saver, session
init = tf.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=20)
sess = tf.compat.v1.Session()
sess.run(init)
create_savedmodel(sess, ["x_cnn:0", "x_rnn:0", "y:0"], ["prediction:0"],
params.outdir)