chore: Remove learning rate decay

Set no learning rate decay by default. If users want to set learning rate decays, they can do that, but the original SSD implementation does not use a smooth learning rate decay, it uses a step function learning rate scheduler instead.
nicocurat · Apr 13, 2018 · 8a7c3ef · 8a7c3ef
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Showing 6 changed files with 11 additions and 11 deletions.
diff --git a/ssd300_evaluation_COCO.ipynb b/ssd300_evaluation_COCO.ipynb
@@ -127,9 +127,9 @@
     "\n",
     "# 3: Compile the model so that Keras won't complain the next time you load it.\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
-    "ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)\n",
+    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",
     "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
    ]

diff --git a/ssd300_evaluation_Pascal_VOC.ipynb b/ssd300_evaluation_Pascal_VOC.ipynb
@@ -114,9 +114,9 @@
     "\n",
     "# 3: Compile the model so that Keras won't complain the next time you load it.\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
-    "ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)\n",
+    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",
     "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
    ]

diff --git a/ssd300_inference.ipynb b/ssd300_inference.ipynb
@@ -118,9 +118,9 @@
     "\n",
     "# 3: Compile the model so that Keras won't complain the next time you load it.\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
-    "ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)\n",
+    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",
     "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
    ]

diff --git a/ssd512_inference.ipynb b/ssd512_inference.ipynb
@@ -119,9 +119,9 @@
     "\n",
     "# 3: Compile the model so that Keras won't complain the next time you load it.\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
-    "ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)\n",
+    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",
     "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
    ]

diff --git a/ssd7_training.ipynb b/ssd7_training.ipynb
@@ -149,7 +149,7 @@
     "\n",
     "# 3: Instantiate an Adam optimizer and the SSD loss function and compile the model\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
     "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",

diff --git a/weight_sampling_tutorial.ipynb b/weight_sampling_tutorial.ipynb
@@ -530,9 +530,9 @@
     "\n",
     "# 3: Instantiate an Adam optimizer and the SSD loss function and compile the model.\n",
     "\n",
-    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)\n",
+    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
     "\n",
-    "ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)\n",
+    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
     "\n",
     "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
    ]