From 3db31444cdeda1e91697ffda155a3d77bd94a31b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Mon, 10 Jun 2019 10:48:00 +0800
Subject: [PATCH] SGD now defaults to lr=0.01 so use 1e-3 explicitely

---
 11_training_deep_neural_networks.ipynb | 32 +++++++++++++++++---------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/11_training_deep_neural_networks.ipynb b/11_training_deep_neural_networks.ipynb
index ea8f71d..a9f798f 100644
--- a/11_training_deep_neural_networks.ipynb
+++ b/11_training_deep_neural_networks.ipynb
@@ -511,7 +511,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\",\n",
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "              metrics=[\"accuracy\"])"
    ]
   },
@@ -582,7 +583,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\",\n",
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "              metrics=[\"accuracy\"])"
    ]
   },
@@ -661,7 +663,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\",\n",
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "              metrics=[\"accuracy\"])"
    ]
   },
@@ -707,7 +710,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\",\n",
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "              metrics=[\"accuracy\"])"
    ]
   },
@@ -866,8 +870,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_A.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\",\n",
-    "              metrics=[\"accuracy\"])"
+    "model_A.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "                optimizer=keras.optimizers.SGD(lr=1e-3),\n",
+    "                metrics=[\"accuracy\"])"
    ]
   },
   {
@@ -908,8 +913,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_B.compile(loss=\"binary_crossentropy\", optimizer=\"sgd\",\n",
-    "              metrics=[\"accuracy\"])"
+    "model_B.compile(loss=\"binary_crossentropy\",\n",
+    "                optimizer=keras.optimizers.SGD(lr=1e-3),\n",
+    "                metrics=[\"accuracy\"])"
    ]
   },
   {
@@ -961,7 +967,8 @@
     "for layer in model_B_on_A.layers[:-1]:\n",
     "    layer.trainable = False\n",
     "\n",
-    "model_B_on_A.compile(loss=\"binary_crossentropy\", optimizer=\"sgd\",\n",
+    "model_B_on_A.compile(loss=\"binary_crossentropy\",\n",
+    "                     optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "                     metrics=[\"accuracy\"])"
    ]
   },
@@ -977,7 +984,8 @@
     "for layer in model_B_on_A.layers[:-1]:\n",
     "    layer.trainable = True\n",
     "\n",
-    "model_B_on_A.compile(loss=\"binary_crossentropy\", optimizer=\"sgd\",\n",
+    "model_B_on_A.compile(loss=\"binary_crossentropy\",\n",
+    "                     optimizer=keras.optimizers.SGD(lr=1e-3),\n",
     "                     metrics=[\"accuracy\"])\n",
     "history = model_B_on_A.fit(X_train_B, y_train_B, epochs=16,\n",
     "                           validation_data=(X_valid_B, y_valid_B))"
@@ -1638,7 +1646,9 @@
     "    keras.layers.Dense(100, activation=\"selu\", kernel_initializer=\"lecun_normal\"),\n",
     "    keras.layers.Dense(10, activation=\"softmax\")\n",
     "])\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])"
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
+    "              metrics=[\"accuracy\"])"
    ]
   },
   {