exp lazyfile_classif_end_to_end_subsample_conv_hand script + param

main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_subsample_conv_hand.yml

 all:
-  dense:
-#  deepfried:
   deepstrom:
 
 base:
@@ -9,21 +7,11 @@ base:
   val_size: {"-v": [10000]}
   seed: {"-a": "range(1)"}
   quiet: ["-q"]
-  dataset: ["--mnist", "--cifar10", "--cifar100", "--svhn"]
-
-dense:
-  network: ["dense"]
-  base:
-  repr_dim: {"-D": [16, 64, 128, 1024]}
-
-deepfried:
-  network: ["deepfriedconvnet"]
-  base:
-  nbstacks: {"-N": [1, 3, 5, 7]}
+  dataset: ["--mnist", "--cifar10", "--svhn"]
 
 deepstrom:
   network: ["deepstrom"]
   base:
   nys_size: {"-m": [4, 8, 16, 64, 128, 256, 512]}
-  kernel: ["-C", "-L"]
+  kernel: ["-C"]
 

main/experiments/scripts/november_2018/end_to_end_subsample_conv_hand_kernel_loss/deepstrom_classif_end_to_end.py

@@ -54,7 +54,7 @@ from tensorflow.python.keras.layers import Dense
 from tensorflow.python.keras.models import Sequential
 
 from skluc.main.tensorflow_.kernel_approximation.fastfood_layer import FastFoodLayer
-from skluc.main.tensorflow_.kernel_approximation.nystrom_layer import DeepstromLayerEndToEnd
+from skluc.main.tensorflow_.kernel_approximation.nystrom_layer import DeepstromLayerEndToEnd, KernelLayerEndToEnd
 from skluc.main.tensorflow_.models import build_lenet_model, build_vgg19_model
 from skluc.main.utils import logger, memory_usage, ParameterManager, ResultManager, ResultPrinter
 from skluc.main.tensorflow_.utils import batch_generator
@@ -155,7 +155,14 @@ def main(paraman, resman, printman):
 
     subsample_indexes = data.get_uniform_class_rand_indices_validation(paraman["--nys-size"])
     nys_subsample = data.validation.data[subsample_indexes]
-
+    y_subsample = data.validation.labels[subsample_indexes]
+
+    y_train_y_subsample = np.zeros((X_train.shape[0], paraman["--nys-size"]))  # la matrice qui, pour chaque exemple du train dit de quel exemple de base il est de la même classe
+    for i in range(X_train.shape[0]):
+        y_train_y_subsample[i][np.where(np.argmax(y_subsample, axis=1) == np.argmax(y_train[i]))[0]] = 1.
+    y_test_y_subsample = np.zeros((X_test.shape[0], paraman["--nys-size"]))  # la matrice qui, pour chaque exemple du test dit de quel exemple de base il est de la même classe
+    for i in range(X_test.shape[0]):
+        y_test_y_subsample[i][np.where((y_subsample == y_test[i]).all())[0]] = 1.
     # # Model definition
 
     input_dim = X_train.shape[1:]
@@ -169,14 +176,33 @@ def main(paraman, resman, printman):
     repr_x = convnet_model(x)
 
     repr_sub = tf.placeholder(tf.float32, shape=[paraman["--nys-size"], *repr_x.shape[1:]], name="subsample_conv_input")
+    y_sub = tf.placeholder(tf.float32, shape=[None, paraman["--nys-size"]], name="subsample_labels")
 
-    deepstrom_layer = DeepstromLayerEndToEnd(subsample_size=paraman["--nys-size"],
+    kernel_layer = KernelLayerEndToEnd(subsample_size=paraman["--nys-size"],
                                              kernel_name=paraman["kernel"],
-                                             kernel_dict=paraman["kernel_dict"],
-                                             activation=paraman["deepstrom_activation"],
-                                             out_dim=paraman["--out-dim"])
+                                             kernel_dict=paraman["kernel_dict"]
+                                          )
+
+    kernel_vec = kernel_layer([repr_x, repr_sub])
 
-    input_classifier = deepstrom_layer([repr_x, repr_sub])
+    with tf.name_scope("mse"):
+        mse_kernel_vec = tf.losses.mean_squared_error(predictions=kernel_vec, labels=y_sub)
+        tf.summary.scalar('mse_kernel_vec', mse_kernel_vec)
+
+    if paraman["deepstrom_activation"] == "tan":
+        activation_fct = tf.nn.tanh
+    elif paraman["deepstrom_activation"] == "relu":
+        activation_fct = tf.nn.relu
+    else:
+        activation_fct = None
+
+    if paraman["--out-dim"] is not None and paraman["--out-dim"] > paraman["--nys-size"]:
+        raise ValueError("Output dim {} is greater than deepstrom subsample size {}. Aborting.".format(paraman["--out-dim"], paraman["--nys-size"]))
+    elif paraman["--out-dim"] is None:
+        output_dim_w_nys =  paraman["--nys-size"]
+    else:
+        output_dim_w_nys = paraman["--out-dim"]
+    input_classifier = Dense(output_dim_w_nys, activation=activation_fct)(kernel_vec)
 
     with tf.variable_scope("classification"):
         classif_layer = Dense(output_dim)
@@ -193,8 +219,11 @@ def main(paraman, resman, printman):
     with tf.name_scope("train"):
         global_step = tf.Variable(0, name="global_step", trainable=False)
         lr = 1e-4
-        train_optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(cross_entropy,
+        train_optimizer_classif = tf.train.AdamOptimizer(learning_rate=lr).minimize(cross_entropy,
+                                                                              global_step=global_step)
+        train_optimizer_kernel_vec = tf.train.AdamOptimizer(learning_rate=lr).minimize(mse_kernel_vec,
                                                                               global_step=global_step)
+        train_op = tf.group(train_optimizer_classif, train_optimizer_kernel_vec)
 
     # calcul de l'accuracy
     with tf.name_scope("accuracy"):
@@ -228,28 +257,35 @@ def main(paraman, resman, printman):
         j = 0
         for i in range(paraman["--num-epoch"]):
             logger.debug(memory_usage())
-
-            for X_batch, Y_batch in batch_generator(X_train, y_train, paraman["--batch-size"], False):
+            k = 0
+            for i_batch, (X_batch, Y_batch) in enumerate(batch_generator(X_train, y_train, paraman["--batch-size"], False)):
+                idx_start_batch = i_batch*paraman["--batch-size"]
+                idx_stop_batch = (i_batch+1)*paraman["--batch-size"]
+                y_train_y_subsample_batch = y_train_y_subsample[idx_start_batch:idx_stop_batch]
+                print(np.argmax(Y_batch, axis=1))
+                print(np.argmax(y_subsample, axis=1))
+                print(y_train_y_subsample_batch)
+                print()
                 if j % paraman["--subs-every"] == 0:
                     feed_dict = {x: nys_subsample}
                     computed_repr_sub, = sess.run([repr_x], feed_dict=feed_dict)
                 try:
-                    feed_dict = {x: X_batch, y: Y_batch, repr_sub: computed_repr_sub}
+                    feed_dict = {x: X_batch, y: Y_batch, repr_sub: computed_repr_sub, y_sub:y_train_y_subsample_batch}
                 except NameError as e:
                     logger.error("A representation of the subsample must have been computed at least once for it to be used in the deepstrom")
                     raise e
 
-                _, loss, acc, summary_str = sess.run([train_optimizer, cross_entropy, accuracy_op, merged_summary], feed_dict=feed_dict)
+                _, loss, acc, summary_str = sess.run([train_op, cross_entropy, accuracy_op, merged_summary], feed_dict=feed_dict)
                 if j % 100 == 0:
                     logger.info(
-                        "epoch: {}/{}; batch: {}/{}; batch_shape: {}; loss: {}; acc: {}".format(i, paraman["--num-epoch"], j + 1,
-                                                                                                int(data.train[0].shape[
-                                                                                                        0] / paraman["--batch-size"]) + 1,
+                        "epoch: {}/{}; batch: {}/{}; batch_shape: {}; loss: {}; acc: {}".format(i, paraman["--num-epoch"],
+                                                                                                k + 1, int(data.train[0].shape[0] / paraman["--batch-size"]) + 1,
                                                                                                 X_batch.shape, loss,
                                                                                                 acc))
                     if paraman["--tensorboard"]:
                         summary_writer.add_summary(summary_str, j)
                 j += 1
+                k += 1
 
         logger.info("Evaluation on validation data")
         training_time = t.time() - global_start