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#KERAS DATA AUGMENTATION ROTATION MOVIE#

Model.fit(x_train, y_train, batch_size=32, epochs=10) pile(loss='categorical_crossentropy', optimizer=sgd) Model.add(MaxPooling2D(pool_size=(2, 2))) # this applies 32 convolution filters of size 3x3 each. VGG-like convnet: from keras.models import Sequentialįrom keras.layers import Dense, Dropout, Flattenįrom keras.layers import Conv2D, MaxPooling2D Model.add(Dense(64, input_dim=20, activation='relu')) MLP for binary classification: model = Sequential() Score = model.evaluate(x_test, y_test, batch_size=128) Model.add(Dense(64, activation='relu', input_dim=20)) # in the first layer, you must specify the expected input data shape: # Dense(64) is a fully-connected layer with 64 hidden units. Multilayer Perceptron (MLP) for multi-class softmax classification: from keras.models import Sequentialįrom keras.layers import Dense, Dropout, Activation Character-level text generation with LSTM.MNIST handwritten digits classification: MLP & CNN.Reuters newswires topic classification: Multilayer Perceptron (MLP).

#KERAS DATA AUGMENTATION ROTATION MOVIE#

IMDB movie review sentiment classification: LSTM over sequences of words.

CIFAR10 small images classification: Convolutional Neural Network (CNN) with realtime data augmentation.

In the examples folder, you will also find example models for real datasets: Here are a few examples to get you started! Model.fit(data, binary_labels, epochs=10, batch_size=32) # Convert labels to categorical one-hot encodingīinary_labels = _categorical(labels, num_classes=10) Model.add(Dense(10, activation='softmax')) # For a single-input model with 10 classes (categorical classification):

Model.fit(data, labels, epochs=10, batch_size=32) # Train the model, iterating on the data in batches of 32 samples Model.add(Dense(1, activation='sigmoid')) Model.add(Dense(32, activation='relu', input_dim=100)) # For a single-input model with 2 classes (binary classification): For training a model, you will typically use the fit function. Keras models are trained on Numpy arrays of input data and labels. # For a mean squared error regression problem # For a multi-class classification problem A metric could be the string identifier of an existing metric or a custom metric function. For any classification problem you will want to set this to metrics=. It can be the string identifier of an existing loss function (such as categorical_crossentropy or mse), or it can be an objective function. This is the objective that the model will try to minimize. This could be the string identifier of an existing optimizer (such as rmsprop or adagrad), or an instance of the Optimizer class. If you pass both batch_size=32 and input_shape=(6, 8) to a layer, it will then expect every batch of inputs to have the batch shape (32, 6, 8).Īs such, the following snippets are strictly equivalent: model = Sequential()īefore training a model, you need to configure the learning process, which is done via the compile method.

If you ever need to specify a fixed batch size for your inputs (this is useful for stateful recurrent networks), you can pass a batch_size argument to a layer.

Some 2D layers, such as Dense, support the specification of their input shape via the argument input_dim, and some 3D temporal layers support the arguments input_dim and input_length.

In input_shape, the batch dimension is not included. This is a shape tuple (a tuple of integers or None entries, where None indicates that any positive integer may be expected).

Pass an input_shape argument to the first layer.

There are several possible ways to do this: For this reason, the first layer in a Sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape. The model needs to know what input shape it should expect. You can create a Sequential model by passing a list of layer instances to the constructor: from keras.models import Sequentialįrom keras.layers import Dense, Activation

The Sequential model is a linear stack of layers. Getting started with the Keras Sequential model