Python tensorflow.python.keras.models.Sequential() Examples
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Example #1
Source File: model.py From cloudml-samples with Apache License 2.0 | 6 votes |
def keras_estimator(model_dir, config, learning_rate, vocab_size): """Creates a Keras Sequential model with layers. Args: model_dir: (str) file path where training files will be written. config: (tf.estimator.RunConfig) Configuration options to save model. learning_rate: (int) Learning rate. vocab_size: (int) Size of the vocabulary in number of words. Returns: A keras.Model """ model = models.Sequential() model.add(Embedding(vocab_size, 16)) model.add(GlobalAveragePooling1D()) model.add(Dense(16, activation=tf.nn.relu)) model.add(Dense(1, activation=tf.nn.sigmoid)) # Compile model with learning parameters. optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) model.compile( optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy']) estimator = tf.keras.estimator.model_to_estimator( keras_model=model, model_dir=model_dir, config=config) return estimator
Example #2
Source File: plot_segment_rep.py From seglearn with BSD 3-Clause "New" or "Revised" License | 6 votes |
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5, conv_filters=3, lstm_units=3): input_shape = (width, n_vars) model = Sequential() model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size, padding='valid', activation='relu', input_shape=input_shape)) model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size, padding='valid', activation='relu')) model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1)) model.add(Dense(n_classes, activation="softmax")) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model # load the data
Example #3
Source File: plot_model_selection2.py From seglearn with BSD 3-Clause "New" or "Revised" License | 6 votes |
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5, conv_filters=2, lstm_units=2): # create a crnn model with keras with one cnn layers, and one rnn layer input_shape = (width, n_vars) model = Sequential() model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size, padding='valid', activation='relu', input_shape=input_shape)) model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1)) model.add(Dense(n_classes, activation="softmax")) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model # load the data
Example #4
Source File: plot_nn_training_curves.py From seglearn with BSD 3-Clause "New" or "Revised" License | 6 votes |
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5, conv_filters=3, lstm_units=3): input_shape = (width, n_vars) model = Sequential() model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size, padding='valid', activation='relu', input_shape=input_shape)) model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1)) model.add(Dense(n_classes, activation="softmax")) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model ############################################## # Setup ############################################## # load the data
Example #5
Source File: prepare_model.py From camera-trap-classifier with MIT License | 6 votes |
def set_last_layer_to_random(model_trained, model_random): """ Set all layers with and after layer_name to random """ logging.info("Replacing layers of model with random layers") layer_names = [x.name for x in model_trained.layers] layer = layer_names[-1] # find layers which have to be kept unchanged id_to_set_random = layer_names.index(layer) # combine old, trained layers with new random layers comb_layers = model_trained.layers[0:id_to_set_random] new_layers = model_random.layers[id_to_set_random:] comb_layers.extend(new_layers) # define new model new_model = Sequential(comb_layers) # print layers of new model for layer, i in zip(new_model.layers, range(0, len(new_model.layers))): logging.info("New model - layer %s: %s" % (i, layer.name)) return new_model
Example #6
Source File: model.py From cloudml-samples with Apache License 2.0 | 5 votes |
def keras_estimator(model_dir, config, params): """Creates a Keras Sequential model with layers. Mean Squared Error (MSE) is a common loss function used for regression. A common regression metric is Mean Absolute Error (MAE). Args: model_dir: (str) file path where training files will be written. config: (tf.estimator.RunConfig) Configuration options to save model. params: (dict) Returns: A keras.Model """ model = models.Sequential() model.add( Dense(64, activation=tf.nn.relu, input_shape=(params['num_features'],))) model.add(Dense(64, activation=tf.nn.relu)) model.add(Dense(1)) # Compile model with learning parameters. optimizer = tf.train.RMSPropOptimizer(learning_rate=params['learning_rate']) model.compile(optimizer=optimizer, loss='mse', metrics=['mae']) return tf.keras.estimator.model_to_estimator( keras_model=model, model_dir=model_dir, config=config)
Example #7
Source File: model.py From cloudml-samples with Apache License 2.0 | 5 votes |
def keras_estimator(model_dir, config, learning_rate): """Creates a Keras Sequential model with layers. Args: model_dir: (str) file path where training files will be written. config: (tf.estimator.RunConfig) Configuration options to save model. learning_rate: (int) Learning rate. Returns: A keras.Model """ model = models.Sequential() model.add(Flatten(input_shape=(28, 28))) model.add(Dense(128, activation=tf.nn.relu)) model.add(Dense(10, activation=tf.nn.softmax)) # Compile model with learning parameters. optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) model.compile( optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy']) estimator = tf.keras.estimator.model_to_estimator( keras_model=model, model_dir=model_dir, config=config) return estimator
Example #8
Source File: prepare_model.py From camera-trap-classifier with MIT License | 5 votes |
def set_specific_layers_to_random(model_trained, model_random, layer): """ Set all layers with and after layer_name to random """ logging.info("Replacing layers of model with random layers") layer_names = [x.name for x in model_trained.layers] # check if target layer is in model if layer not in layer_names: logging.error("Layer %s not in model.layers" % layer) logging.error("Available Layers %s" % layer_names) raise IOError("Layer %s not in model.layers" % layer) # find layers which have to be kept unchanged id_to_set_random = layer_names.index(layer) # combine old, trained layers with new random layers comb_layers = model_trained.layers[0:id_to_set_random] new_layers = model_random.layers[id_to_set_random:] comb_layers.extend(new_layers) # define new model new_model = Sequential(comb_layers) # print layers of new model for layer, i in zip(new_model.layers, range(0, len(new_model.layers))): logging.debug("New model - layer %s: %s" % (i, layer.name)) return new_model
Example #9
Source File: keras.py From estimator with Apache License 2.0 | 5 votes |
def _assert_valid_model(model, custom_objects=None): is_subclass = (not model._is_graph_network and not isinstance(model, models.Sequential)) if is_subclass: try: custom_objects = custom_objects or {} with tf.keras.utils.CustomObjectScope(custom_objects): model.__class__.from_config(model.get_config()) except NotImplementedError: raise ValueError( 'Subclassed `Model`s passed to `model_to_estimator` must ' 'implement `Model.get_config` and `Model.from_config`.')
Example #10
Source File: tsne_grid.py From tsne-grid with MIT License | 5 votes |
def build_model(): base_model = VGG16(weights='imagenet') top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) return Model(inputs=base_model.input, outputs=top_model(base_model.output))
Example #11
Source File: conv_network.py From mnist_digits_classification with MIT License | 4 votes |
def model(train_x, train_y, test_x, test_y, epoch): ''' :param train_x: train features :param train_y: train labels :param test_x: test features :param test_y: test labels :param epoch: no. of epochs :return: ''' conv_model = Sequential() # first layer with input shape (img_rows, img_cols, 1) and 12 filters conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu', input_shape=(img_rows, img_cols, 1))) # second layer with 12 filters conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu')) # third layer with 12 filers conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu')) # flatten layer conv_model.add(Flatten()) # adding a Dense layer conv_model.add(Dense(100, activation='relu')) # adding the final Dense layer with softmax conv_model.add(Dense(num_classes, activation='softmax')) # compile the model conv_model.compile(optimizer=keras.optimizers.Adadelta(), loss='categorical_crossentropy', metrics=['accuracy']) print("\n Training the Convolution Neural Network on MNIST data\n") # fit the model conv_model.fit(train_x, train_y, batch_size=128, epochs=epoch, validation_split=0.1, verbose=2) predicted_train_y = conv_model.predict(train_x) train_accuracy = (sum(np.argmax(predicted_train_y, axis=1) == np.argmax(train_y, axis=1))/(float(len(train_y)))) print('Train accuracy : ', train_accuracy) predicted_test_y = conv_model.predict(test_x) test_accuracy = (sum(np.argmax(predicted_test_y, axis=1) == np.argmax(test_y, axis=1))/(float(len(test_y)))) print('Test accuracy : ', test_accuracy) CNN_accuracy = {'train_accuracy': train_accuracy, 'test_accuracy': test_accuracy, 'epoch': epoch} return conv_model, CNN_accuracy