diff --git a/main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_subsample_conv_hand.yml b/main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_subsample_conv_hand.yml
index 738deb29e4ab5c9374c5b0ae5df44a986bd0d68a..4be003dc5a4eff16caeb0f4016013f4eca268343 100644
--- a/main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_subsample_conv_hand.yml
+++ b/main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_subsample_conv_hand.yml
@@ -1,6 +1,4 @@
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"]
diff --git a/main/experiments/scripts/november_2018/end_to_end_subsample_conv_hand_kernel_loss/deepstrom_classif_end_to_end.py b/main/experiments/scripts/november_2018/end_to_end_subsample_conv_hand_kernel_loss/deepstrom_classif_end_to_end.py
index 30b5b4b08d6b0c1e4e7d6f88f08e5ed8cb0c1ea1..12bc54a2de4270fed00f94ad6fe35b0520610109 100644
--- a/main/experiments/scripts/november_2018/end_to_end_subsample_conv_hand_kernel_loss/deepstrom_classif_end_to_end.py
+++ b/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])
+
+ 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
- input_classifier = deepstrom_layer([repr_x, repr_sub])
+ 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