diff --git a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb
index 388329c85b5..289d5f2d83a 100644
--- a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb
+++ b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb
@@ -216,7 +216,7 @@
"model = keras.models.Sequential([\n",
" keras.layers.Dense(1, input_shape=[window_size])\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -244,7 +244,7 @@
"\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-6 * 10**(epoch / 30))\n",
- "optimizer = keras.optimizers.SGD(lr=1e-6, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-6, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -282,7 +282,7 @@
"model = keras.models.Sequential([\n",
" keras.layers.Dense(1, input_shape=[window_size])\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -387,7 +387,7 @@
"\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-7 * 10**(epoch / 20))\n",
- "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-7, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -428,7 +428,7 @@
" keras.layers.Dense(1)\n",
"])\n",
"\n",
- "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
diff --git a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c06_forecasting_with_rnn.ipynb b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c06_forecasting_with_rnn.ipynb
index 962046f7237..f0eb58a4953 100644
--- a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c06_forecasting_with_rnn.ipynb
+++ b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c06_forecasting_with_rnn.ipynb
@@ -211,7 +211,7 @@
"])\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-7 * 10**(epoch / 20))\n",
- "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-7, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -254,7 +254,7 @@
" keras.layers.Dense(1),\n",
" keras.layers.Lambda(lambda x: x * 200.0)\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1.5e-6, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1.5e-6, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -382,7 +382,7 @@
"])\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-7 * 10**(epoch / 30))\n",
- "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-7, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -426,7 +426,7 @@
" keras.layers.Dense(1),\n",
" keras.layers.Lambda(lambda x: x * 200.0)\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1e-6, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-6, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
diff --git a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c07_forecasting_with_stateful_rnn.ipynb b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c07_forecasting_with_stateful_rnn.ipynb
index b12d836eae8..a77d0b39a2e 100644
--- a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c07_forecasting_with_stateful_rnn.ipynb
+++ b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c07_forecasting_with_stateful_rnn.ipynb
@@ -234,7 +234,7 @@
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-8 * 10**(epoch / 30))\n",
"reset_states = ResetStatesCallback()\n",
- "optimizer = keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-8, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -277,7 +277,7 @@
" keras.layers.Dense(1),\n",
" keras.layers.Lambda(lambda x: x * 200.0)\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-7, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
diff --git a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c08_forecasting_with_lstm.ipynb b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c08_forecasting_with_lstm.ipynb
index e8dfba8208a..24fdbbbf6c9 100644
--- a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c08_forecasting_with_lstm.ipynb
+++ b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c08_forecasting_with_lstm.ipynb
@@ -214,7 +214,7 @@
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-8 * 10**(epoch / 20))\n",
"reset_states = ResetStatesCallback()\n",
- "optimizer = keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-8, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -257,7 +257,7 @@
" keras.layers.Dense(1),\n",
" keras.layers.Lambda(lambda x: x * 200.0)\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=5e-7, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=5e-7, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
diff --git a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c09_forecasting_with_cnn.ipynb b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c09_forecasting_with_cnn.ipynb
index fc72b7a9ef0..86cb7f64601 100644
--- a/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c09_forecasting_with_cnn.ipynb
+++ b/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c09_forecasting_with_cnn.ipynb
@@ -216,7 +216,7 @@
"])\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-8 * 10**(epoch / 20))\n",
- "optimizer = keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-8, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -263,7 +263,7 @@
" keras.layers.Dense(1),\n",
" keras.layers.Lambda(lambda x: x * 200)\n",
"])\n",
- "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
+ "optimizer = keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -362,7 +362,7 @@
"model.add(keras.layers.Conv1D(filters=1, kernel_size=1))\n",
"lr_schedule = keras.callbacks.LearningRateScheduler(\n",
" lambda epoch: 1e-4 * 10**(epoch / 30))\n",
- "optimizer = keras.optimizers.Adam(lr=1e-4)\n",
+ "optimizer = keras.optimizers.Adam(learning_rate=1e-4)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",
@@ -411,7 +411,7 @@
" activation=\"relu\")\n",
" )\n",
"model.add(keras.layers.Conv1D(filters=1, kernel_size=1))\n",
- "optimizer = keras.optimizers.Adam(lr=3e-4)\n",
+ "optimizer = keras.optimizers.Adam(learning_rate=3e-4)\n",
"model.compile(loss=keras.losses.Huber(),\n",
" optimizer=optimizer,\n",
" metrics=[\"mae\"])\n",