Remove \n, ' ', and '' again

scripts/generate_tf_guides.py

@@ -136,9 +136,8 @@
 
 
 def generate_single_tf_guide(source_dir, target_dir, title, source_name, target_name):
-    f = open(Path(source_dir) / (source_name + ".ipynb"))
-    original_ipynb = json.loads(f.read())
-    f.close()
+    nb = (Path(source_dir) / source_name).with_suffix(".ipynb")
+    original_ipynb = json.loads(nb.read_text())
 
     # Skip first title cell
     cells = original_ipynb["cells"][1:]
@@ -164,6 +163,13 @@ def generate_single_tf_guide(source_dir, target_dir, title, source_name, target_
             if len(lines) < 2:
                 new_lines.append(lines[-1])
             cell["source"] = new_lines
+        elif cell["cell_type"] == "code":
+          lines = cell["source"]
+          if not lines[0].strip():
+            lines = lines[1:]
+          if not lines[-1].strip():
+            lines = lines[:-1]
+          cell["source"] = lines
 
     # Add header cells
     header_cells = copy.deepcopy(TF_IPYNB_CELLS_TEMPLATE)

tf/custom_callback.ipynb

@@ -203,8 +203,7 @@
     "        loss=\"mean_squared_error\",\n",
     "        metrics=[\"mean_absolute_error\"],\n",
     "    )\n",
-    "    return model\n",
-    ""
+    "    return model\n"
    ]
   },
   {
@@ -263,7 +262,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomCallback(keras.callbacks.Callback):\n",
     "    def on_train_begin(self, logs=None):\n",
     "        keys = list(logs.keys())\n",
@@ -319,8 +317,7 @@
     "\n",
     "    def on_predict_batch_end(self, batch, logs=None):\n",
     "        keys = list(logs.keys())\n",
-    "        print(\"...Predicting: end of batch {}; got log keys: {}\".format(batch, keys))\n",
-    ""
+    "        print(\"...Predicting: end of batch {}; got log keys: {}\".format(batch, keys))\n"
    ]
   },
   {
@@ -381,7 +378,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class LossAndErrorPrintingCallback(keras.callbacks.Callback):\n",
     "    def on_train_batch_end(self, batch, logs=None):\n",
     "        print(\"For batch {}, loss is {:7.2f}.\".format(batch, logs[\"loss\"]))\n",
@@ -562,7 +558,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomLearningRateScheduler(keras.callbacks.Callback):\n",
     "    \"\"\"Learning rate scheduler which sets the learning rate according to schedule.\n",
     "\n",

tf/custom_layers_and_models.ipynb

@@ -115,7 +115,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32, input_dim=32):\n",
     "        super(Linear, self).__init__()\n",
@@ -130,8 +129,7 @@
     "        )\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return tf.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return tf.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -203,7 +201,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32, input_dim=32):\n",
     "        super(Linear, self).__init__()\n",
@@ -247,7 +244,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class ComputeSum(keras.layers.Layer):\n",
     "    def __init__(self, input_dim):\n",
     "        super(ComputeSum, self).__init__()\n",
@@ -314,7 +310,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32, input_dim=32):\n",
     "        super(Linear, self).__init__()\n",
@@ -324,8 +319,7 @@
     "        self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return tf.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return tf.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -352,7 +346,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32):\n",
     "        super(Linear, self).__init__()\n",
@@ -369,8 +362,7 @@
     "        )\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return tf.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return tf.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -482,8 +474,7 @@
     "\n",
     "    def call(self, inputs):\n",
     "        self.add_loss(self.rate * tf.reduce_sum(inputs))\n",
-    "        return inputs\n",
-    ""
+    "        return inputs\n"
    ]
   },
   {
@@ -508,7 +499,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class OuterLayer(keras.layers.Layer):\n",
     "    def __init__(self):\n",
     "        super(OuterLayer, self).__init__()\n",
@@ -549,7 +539,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class OuterLayerWithKernelRegularizer(keras.layers.Layer):\n",
     "    def __init__(self):\n",
     "        super(OuterLayerWithKernelRegularizer, self).__init__()\n",
@@ -666,7 +655,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class LogisticEndpoint(keras.layers.Layer):\n",
     "    def __init__(self, name=None):\n",
     "        super(LogisticEndpoint, self).__init__(name=name)\n",
@@ -685,8 +673,7 @@
     "        self.add_metric(acc, name=\"accuracy\")\n",
     "\n",
     "        # Return the inference-time prediction tensor (for `.predict()`).\n",
-    "        return tf.nn.softmax(logits)\n",
-    ""
+    "        return tf.nn.softmax(logits)\n"
    ]
   },
   {
@@ -775,7 +762,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32):\n",
     "        super(Linear, self).__init__()\n",
@@ -827,7 +813,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class Linear(keras.layers.Layer):\n",
     "    def __init__(self, units=32, **kwargs):\n",
     "        super(Linear, self).__init__(**kwargs)\n",
@@ -906,7 +891,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomDropout(keras.layers.Layer):\n",
     "    def __init__(self, rate, **kwargs):\n",
     "        super(CustomDropout, self).__init__(**kwargs)\n",
@@ -915,8 +899,7 @@
     "    def call(self, inputs, training=None):\n",
     "        if training:\n",
     "            return tf.nn.dropout(inputs, rate=self.rate)\n",
-    "        return inputs\n",
-    ""
+    "        return inputs\n"
    ]
   },
   {
@@ -1122,8 +1105,7 @@
     "            z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1\n",
     "        )\n",
     "        self.add_loss(kl_loss)\n",
-    "        return reconstructed\n",
-    ""
+    "        return reconstructed\n"
    ]
   },
   {

tf/customizing_what_happens_in_fit.ipynb

@@ -170,7 +170,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomModel(keras.Model):\n",
     "    def train_step(self, data):\n",
     "        # Unpack the data. Its structure depends on your model and\n",
@@ -191,8 +190,7 @@
     "        # Update metrics (includes the metric that tracks the loss)\n",
     "        self.compiled_metrics.update_state(y, y_pred)\n",
     "        # Return a dict mapping metric names to current value\n",
-    "        return {m.name: m.result() for m in self.metrics}\n",
-    ""
+    "        return {m.name: m.result() for m in self.metrics}\n"
    ]
   },
   {
@@ -319,7 +317,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomModel(keras.Model):\n",
     "    def train_step(self, data):\n",
     "        # Unpack the data. Its structure depends on your model and\n",
@@ -391,7 +388,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomModel(keras.Model):\n",
     "    def test_step(self, data):\n",
     "        # Unpack the data\n",
@@ -504,7 +500,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class GAN(keras.Model):\n",
     "    def __init__(self, discriminator, generator, latent_dim):\n",
     "        super(GAN, self).__init__()\n",
@@ -560,8 +555,7 @@
     "            g_loss = self.loss_fn(misleading_labels, predictions)\n",
     "        grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
     "        self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n",
-    "        return {\"d_loss\": d_loss, \"g_loss\": g_loss}\n",
-    ""
+    "        return {\"d_loss\": d_loss, \"g_loss\": g_loss}\n"
    ]
   },
   {

tf/functional.ipynb

@@ -613,7 +613,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "def get_model():\n",
     "    inputs = keras.Input(shape=(128,))\n",
     "    outputs = layers.Dense(1)(inputs)\n",
@@ -1129,7 +1128,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomDense(layers.Layer):\n",
     "    def __init__(self, units=32):\n",
     "        super(CustomDense, self).__init__()\n",
@@ -1175,7 +1173,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomDense(layers.Layer):\n",
     "    def __init__(self, units=32):\n",
     "        super(CustomDense, self).__init__()\n",

tf/masking_and_padding.ipynb

@@ -177,8 +177,7 @@
     "padded_inputs = tf.keras.preprocessing.sequence.pad_sequences(\n",
     "    raw_inputs, padding=\"post\"\n",
     ")\n",
-    "print(padded_inputs)\n",
-    ""
+    "print(padded_inputs)\n"
    ]
   },
   {
@@ -346,7 +345,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class MyLayer(layers.Layer):\n",
     "    def __init__(self, **kwargs):\n",
     "        super(MyLayer, self).__init__(**kwargs)\n",
@@ -409,7 +407,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class TemporalSplit(keras.layers.Layer):\n",
     "    \"\"\"Split the input tensor into 2 tensors along the time dimension.\"\"\"\n",
     "\n",
@@ -450,7 +447,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class CustomEmbedding(keras.layers.Layer):\n",
     "    def __init__(self, input_dim, output_dim, mask_zero=False, **kwargs):\n",
     "        super(CustomEmbedding, self).__init__(**kwargs)\n",
@@ -516,16 +512,14 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class MyActivation(keras.layers.Layer):\n",
     "    def __init__(self, **kwargs):\n",
     "        super(MyActivation, self).__init__(**kwargs)\n",
     "        # Signal that the layer is safe for mask propagation\n",
     "        self.supports_masking = True\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return tf.nn.relu(inputs)\n",
-    ""
+    "        return tf.nn.relu(inputs)\n"
    ]
   },
   {
@@ -587,7 +581,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class TemporalSoftmax(keras.layers.Layer):\n",
     "    def call(self, inputs, mask=None):\n",
     "        broadcast_float_mask = tf.expand_dims(tf.cast(mask, \"float32\"), -1)\n",