Programming Language: Python

Namespace/Package Name: keras.models

Class/Type: Sequential

Examples at hotexamples.com: 48

Python Keras is a high-level neural networks API, written in Python and capable of running on top of several lower-level neural network libraries, including TensorFlow, Microsoft Cognitive Toolkit, or Theano. Keras makes it easy to construct a deep learning model by allowing users to define the architecture of the model layer by layer.

The `keras.models.Sequential` is a type of model that is linear and sequential in nature. This means that each layer in the model feeds into the next layer and so on until the final output is produced. The Sequential model is the simplest model in Keras to construct and is perfect for beginners to start with.

**Example 1:** The following code creates a basic Sequential model with two dense layers and an output layer using the sigmoid activation function:

**Example 2:** The following code constructs a Sequential model that uses Convolutional Neural Network (CNN) layers:

The `keras.models.Sequential` is a type of model that is linear and sequential in nature. This means that each layer in the model feeds into the next layer and so on until the final output is produced. The Sequential model is the simplest model in Keras to construct and is perfect for beginners to start with.

from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(10, input_dim=5, activation='relu')) model.add(Dense(5, activation='relu')) model.add(Dense(1, activation='sigmoid'))

import keras from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=(28, 28, 1), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dense(10, activation='softmax'))In both examples, `keras.models.Sequential` is used to define the linear architecture of the model. The first example creates a simple fully connected neural network with three dense layers, whereas the second example uses CNN layers to extract features from images. Keras is a deep learning library that sits on top of TensorFlow or Theano, and provides an API to make it easier to build deep learning models.

Frequently Used Methods

Frequently Used Methods

Frequently Used Methods

Related in langs

Frequently Used Methods

Related