Tensorflow daily one

Daily_Conversations/Tensorflow随笔(一).md

+TensorFlow is an end-to-end open source platform for machine learning
+
+TensorFlow makes it easy for beginners and experts to create machine learning models. See the sections below to get started.
+
+
+
+https://www.tensorflow.org/tutorials
+
+Tutorials show you how to use TensorFlow with complete, end-to-end examples
+
+
+
+https://www.tensorflow.org/guide
+
+Guides explain the concepts and components of TensorFlow.
+
+
+
+#### For beginners
+
+The best place to start is with the user-friendly Sequential API. You can create models by plugging together building blocks. Run the “Hello World” example below, then visit the [tutorials](https://www.tensorflow.org/tutorials) to learn more.
+
+To learn ML, check out our [education page](https://www.tensorflow.org/resources/learn-ml). Begin with curated curriculums to improve your skills in foundational ML areas.
+
+```
+import tensorflow as tf
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train),(x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=5)
+model.evaluate(x_test, y_test)
+```
+
+#### For experts
+
+```
+class MyModel(tf.keras.Model):
+  def __init__(self):
+    super(MyModel, self).__init__()
+    self.conv1 = Conv2D(32, 3, activation='relu')
+    self.flatten = Flatten()
+    self.d1 = Dense(128, activation='relu')
+    self.d2 = Dense(10, activation='softmax')
+
+  def call(self, x):
+    x = self.conv1(x)
+    x = self.flatten(x)
+    x = self.d1(x)
+    return self.d2(x)
+model = MyModel()
+
+with tf.GradientTape() as tape:
+  logits = model(images)
+  loss_value = loss(logits, labels)
+grads = tape.gradient(loss_value, model.trainable_variables)
+optimizer.apply_gradients(zip(grads, model.trainable_variables))
+```
+
+### Learn about the relationship between TensorFlow and Keras
+
+TensorFlow's high-level APIs are based on the Keras API standard for defining and training neural networks. Keras enables fast prototyping, state-of-the-art research, and production—all with user-friendly APIs.
+
+## Solutions to common problems
+
+Explore step-by-step tutorials to help you with your projects.
+
+https://www.tensorflow.org/tutorials/keras/classification
+
+https://www.tensorflow.org/tutorials/generative/dcgan
+
+https://www.tensorflow.org/tutorials/text/nmt_with_attention
+
+## News & announcements
+
+Check out our [blog](https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20) for additional updates, and subscribe to our monthly TensorFlow newsletter to get the latest announcements sent directly to your inbox.
+