The complete code to create a simple model is shown below − Model = Model(inputs = data, outputs = layer) Tensor("dense_1/add:0", shape =(?, 2, 2), dtype = float32)Ĭreate a model in functional way by specifying both input and output layer − Now, create an input layer specifying input dimension shape for the model using the below code −ĭefine layer for the input using the below module −Īdd Dense layer for the input using the below line of code − Import an input layer using the below module − This section explains about functional model in brief. First, we create an instance for model and connecting to the layers to access input and output to the model. Functional model, you can define multiple input or output that share layers. Functional API is an alternative approach of creating more complex models. Sequential API is used to create models layer-by-layer. Predict − Predict the results for new input. They are as follows −Ĭompile − Configure the learning process of the modelįit − Train the model using the training dataĮvaluate − Evaluate the model using the test data Model provides function for training, evaluation and prediction process.
#Sequential model full#
Keras provides a simple method, summary to get the full information about the model and its layers.Ī summary of the model created in the previous section is as follows − Understanding the model is very important phase to properly use it for training and prediction purposes. Model_from_yaml() − Accepts yaml representation of the model and create a new model.
Use_bias: true\n name: sequential_10\nkeras_version: 2.2.5\n' Scale: 1.0\n seed: null\n kernel_regularizer: null\n name: dense _15\n Kernel_initializer:\n class_name: VarianceScaling\n config:\n > json_string '\n bias_regu larizer: null\nĭtype: float32\n kernel_constraint: null\n To_json() − Returns the model as an json object. Get_config() − IReturns the model as an object.įrom_config() − It accept the model configuration object as argument and create the model accordingly. Keras provides methods to serialize the model into object as well as json and load it again later. t_weights(weight_numpy_array) − Set the weights of the model. Model.get_weights − Returns all the weights as NumPy arrays. Model.outputs − Returns all the output tensors of the model as list. Model.inputs − Returns all the input tensors of the model as list. Model.layers − Returns all the layers of the model as list. Keras provides few methods to get the model information like layers, input data and output data. Here, we have created one input layer, one hidden layer and one output layer. Hidden_layer = Dense(64, activation='relu') model.add(hidden_layer) Input_layer = Dense(32, input_shape=(8,)) model.add(input_layer) To add a layer, simply create a layer using Keras layer API and then pass the layer through add() function as specified below − Most of the ANN also has layers in sequential order and the data flows from one layer to another layer in the given order until the data finally reaches the output layer.Ī ANN model can be created by simply calling Sequential() API as specified below − The core idea of Sequential API is simply arranging the Keras layers in a sequential order and so, it is called Sequential API. Let us learn now to create model using both Sequential and Functional API in this chapter. Keras provides a two mode to create the model, simple and easy to use Sequential API as well as more flexible and advanced Functional API. As learned earlier, Keras model represents the actual neural network model.
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