Hello I am new in Keras and I am trying to get weight in Keras.

I know how to get weight in Tensorflow in Python. Code below:

`data = np.array(attributes, 'int64') target = np.array(labels, 'int64') feature_columns = [tf.contrib.layers.real_valued_column("", dimension=2, dtype=tf.float32)] learningRate = 0.1 epoch = 10000 # https://www.tensorflow.org/api_docs/python/tf/metrics validation_metrics = { "accuracy": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_accuracy , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "precision": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_precision , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "recall": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_recall , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "mean_absolute_error": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_mean_absolute_error , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "false_negatives": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_false_negatives , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "false_positives": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_false_positives , prediction_key = tf.contrib.learn.PredictionKey.CLASSES), "true_positives": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_true_positives , prediction_key = tf.contrib.learn.PredictionKey.CLASSES) } # validation monitor validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(data, target, every_n_steps=500, metrics = validation_metrics) classifier = tf.contrib.learn.DNNClassifier( feature_columns = feature_columns, hidden_units = [3], activation_fn = tf.nn.sigmoid, optimizer = tf.train.GradientDescentOptimizer(learningRate), model_dir = "model", config = tf.contrib.learn.RunConfig(save_checkpoints_secs = 1) ) classifier.fit(data, target, steps = epoch, monitors = [validation_monitor]) # print('Params:', classifier.get_variable_names()) ''' Params: ['dnn/binary_logistic_head/dnn/learning_rate', 'dnn/hiddenlayer_0/biases', 'dnn/hiddenlayer_0/weights', 'dnn/logits/biases', 'dnn/logits/weights', 'global_step'] ''' print('total steps:', classifier.get_variable_value("global_step")) print('weight from input layer to hidden layer: ', classifier.get_variable_value("dnn/hiddenlayer_0/weights")) print('weight from hidden layer to output layer: ', classifier.get_variable_value("dnn/logits/weights")) `

Is there anyway to obtain weight from (in Keras) like in Tensorflow:

- weight from input layer to hidden layer
- weight from hidden layer to output layer

This is my model in Keras:

`model = Sequential() model.add(Flatten(input_shape=x_train.shape[1:])) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) `