Initial commit

Tensorflow/concours_foetus/train.py

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+import tensorflow as tf
+import sys
+import time
+import cv2
+import numpy as np
+import common
+import config
+import model
+
+images, labels=common.prepare_data('training_set.csv')
+images=np.array(images, dtype=np.float32)/255
+labels=np.array(labels, dtype=np.float32)
+index=np.random.permutation(len(images))
+images=images[index].reshape(-1, config.hauteur, config.largeur, 1)
+labels=labels[index]
+
+print("Nbr images:", len(images))
+
+train_ds=tf.data.Dataset.from_tensor_slices((images, labels)).batch(config.batch_size)
+
+del images
+del labels
+
+def my_loss(labels, preds):
+    lambda_xy=5
+    lambda_Aa=5
+    lambda_angle=1
+    
+    preds_xy=preds[:, 0:2]
+    preds_Aa=preds[:, 2:4]
+    preds_angle=preds[:, 4]
+
+    labels_xy=labels[:, 0:2]
+    labels_Aa=labels[:, 2:4]
+    labels_angle=labels[:, 4]
+    
+    loss_xy=tf.reduce_sum(tf.math.square(preds_xy-labels_xy), axis=-1)
+    loss_Aa=tf.reduce_sum(tf.math.square(preds_Aa-labels_Aa), axis=-1)
+    loss_angle=tf.math.square(preds_angle-labels_angle)
+    
+    loss=lambda_xy*loss_xy+lambda_Aa*loss_Aa+lambda_angle*loss_angle
+    return loss
+
+model=model.model(config.input_model)
+
+@tf.function
+def train_step(images, labels):
+  with tf.GradientTape() as tape:
+    predictions=model(images)
+    loss=my_loss(labels, predictions)
+  gradients=tape.gradient(loss, model.trainable_variables)
+  optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+  train_loss(loss)
+
+def train(train_ds, nbr_entrainement):
+    for entrainement in range(nbr_entrainement):
+        start=time.time()
+        for images, labels in train_ds:
+            train_step(images, labels)
+        message='Entrainement {:04d}: loss: {:6.4f}, temps: {:7.4f}'
+        print(message.format(entrainement+1,
+                             train_loss.result(),
+                             time.time()-start))
+        if not entrainement%10:
+            checkpoint.save(file_prefix="./training/")
+    
+optimizer=tf.keras.optimizers.Adam(learning_rate=1E-4)
+checkpoint=tf.train.Checkpoint(model=model)
+train_loss=tf.keras.metrics.Mean()
+
+checkpoint=tf.train.Checkpoint(model=model)
+checkpoint.restore(tf.train.latest_checkpoint("./training/"))
+
+train(train_ds, 60)
+checkpoint.save(file_prefix="./training/")