Saved weights for trained RNN and LSTM models

LSTMHandler.py

@@ -42,7 +42,7 @@ def MyMetrics(y_true, y_pred):
 class LSTMHandler:
 
 	results_directory = './LSTMresults'
-	models_directory = './LSTMmodels'
+	models_directory = './LSTMmodels/'
 
 	def __init__(self, model_name, num_categories, loss, optimizer):
 		# GET THE MODEL
@@ -88,7 +88,8 @@ def fit_and_eval(self, x_train, y_train, x_test, y_test, nb_epoch, dataset_name)
 				res_recall.append(recall)
 				res_fscore.append(fscore)
 			self.write_result(str(i) +'|'+ str(loss) +'|'+ str(accuracy) +'|'+ str(precision) +'|'+ str(recall) +'|'+ str(fscore))
-
+			self.model.save(self.models_directory + 'full_models/' + dataset_name + '.' + self.model_name + '.epoch' + str(i) + '.h5')
+		self.model.save(self.models_directory + 'full_models/' + dataset_name + '.' + self.model_name + '.h5')
 		return (res_loss, res_accuracy, res_precision, res_recall, res_fscore)
 
 

LSTM_2C.py

@@ -0,0 +1,27 @@
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, LSTM
+from keras.utils.vis_utils import plot_model
+import os
+import numpy as np
+from reader import Reader
+
+
+length = Reader.getInputShape()
+
+model = Sequential()
+
+#EXPECTS INPUT AS (nb_sample, timesteps, nb_features), where nb_sample=1 (batch_size = 1), timesteps = 1 and nb_features = length
+
+#model.add(Dense(40, input_dim = 12, init='uniform', activation='relu'))
+model.add(LSTM(units=50, input_shape=(1,length), batch_input_shape=(1,1,length), recurrent_initializer='random_uniform', kernel_initializer='random_uniform', activation='sigmoid', return_sequences=True, stateful=True))
+model.add(LSTM(units=50, recurrent_initializer='random_uniform', kernel_initializer='random_uniform', activation='sigmoid', stateful=True))
+model.add(Dense(5, kernel_initializer='random_uniform', activation = 'softmax'))
+
+
+model.summary()
+plot_model(model, to_file='./LSTMmodels/LSTM_2C.png', show_shapes=True)
+fp = open('./LSTMmodels/LSTM_2C.json', 'w')
+fp.write(model.to_json())
+fp.close()
+

LSTM_plot_all.py

@@ -22,9 +22,9 @@
 #(4, 'Dataset4', 2, 'RNN_1A')
 #(5, 'Dataset5', 3, 'RNN_1B') 
 #(6, 'Dataset0', 5, 'RNN_1C')
-dataset_id = 3
-dataset_name = 'Dataset3'
-num_classes = 2
+dataset_id = 0
+dataset_name = 'Dataset0'
+num_classes = 5
 #RNN_name = 'RNN_1A'
 
 num_epochs = 10
@@ -35,9 +35,9 @@
 #x_test = x_test[0:1000,:]
 #y_test = y_test[0:1000]
 
-for LSTM_name in ['LSTM_1.2A','LSTM_2.2A', 'LSTM_3.2A']:
+for LSTM_name in ['LSTM_2C']:
 	results = {}
-	for loss,optimizer in [('binary_crossentropy','rmsprop')]:	#categorical_crossentropy
+	for loss,optimizer in [('mse','sgd')]:	#categorical_crossentropy
 	#for optimizer in ['sgd', 'rmsprop']:
 		LSTMmodel = LSTMHandler.LSTMHandler(LSTM_name, num_classes, loss, optimizer)
 		(res_loss, res_accuracy, res_precision, res_recall, res_fscore) = LSTMmodel.fit_and_eval(x_train, y_train, x_test, y_test, num_epochs, dataset_name)

LSTMmodels/LSTM_2C.json

@@ -0,0 +1 @@
+{"config": [{"config": {"recurrent_activation": "hard_sigmoid", "recurrent_initializer": {"config": {"seed": null, "maxval": 0.05, "minval": -0.05}, "class_name": "RandomUniform"}, "implementation": 0, "bias_constraint": null, "unit_forget_bias": true, "units": 50, "trainable": true, "activation": "sigmoid", "return_sequences": true, "use_bias": true, "name": "lstm_1", "bias_initializer": {"config": {}, "class_name": "Zeros"}, "recurrent_regularizer": null, "dropout": 0.0, "recurrent_dropout": 0.0, "kernel_regularizer": null, "dtype": "float32", "bias_regularizer": null, "activity_regularizer": null, "unroll": false, "stateful": true, "batch_input_shape": [1, 1, 79], "kernel_initializer": {"config": {"seed": null, "maxval": 0.05, "minval": -0.05}, "class_name": "RandomUniform"}, "kernel_constraint": null, "recurrent_constraint": null, "go_backwards": false}, "class_name": "LSTM"}, {"config": {"recurrent_activation": "hard_sigmoid", "recurrent_initializer": {"config": {"seed": null, "maxval": 0.05, "minval": -0.05}, "class_name": "RandomUniform"}, "implementation": 0, "bias_constraint": null, "unit_forget_bias": true, "units": 50, "trainable": true, "activation": "sigmoid", "return_sequences": false, "dropout": 0.0, "name": "lstm_2", "bias_initializer": {"config": {}, "class_name": "Zeros"}, "recurrent_regularizer": null, "use_bias": true, "recurrent_dropout": 0.0, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "unroll": false, "stateful": true, "kernel_initializer": {"config": {"seed": null, "maxval": 0.05, "minval": -0.05}, "class_name": "RandomUniform"}, "kernel_constraint": null, "recurrent_constraint": null, "go_backwards": false}, "class_name": "LSTM"}, {"config": {"trainable": true, "use_bias": true, "bias_constraint": null, "units": 5, "activation": "softmax", "bias_regularizer": null, "activity_regularizer": null, "name": "dense_1", "kernel_regularizer": null, "kernel_initializer": {"config": {"seed": null, "maxval": 0.05, "minval": -0.05}, "class_name": "RandomUniform"}, "kernel_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}}, "class_name": "Dense"}], "backend": "theano", "class_name": "Sequential", "keras_version": "2.0.3"}

LSTMmodels/full_models/Dataset0.LSTM_2C/Dataset0.LSTM_2C

@@ -0,0 +1,60 @@
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.14650396882|0.581|0|0|0
+2|0.134933630332|0.581|0|0|0
+3|0.12807858073|0.581|0|0|0
+4|0.125045746896|0.581|0|0|0
+5|0.123950693227|0.581|0|0|0
+6|0.123648752145|0.581|0|0|0
+7|0.123634647191|0.581|0|0|0
+8|0.123708837941|0.581|0|0|0
+9|0.123797330961|0.581|0|0|0
+10|0.123875424441|0.581|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.146884530544|0.579|0|0|0
+2|0.135687147945|0.579|0|0|0
+3|0.128945879199|0.579|0|0|0
+4|0.125824560031|0.579|0|0|0
+5|0.124595511615|0.579|0|0|0
+6|0.124180880323|0.579|0|0|0
+7|0.124085704312|0.579|0|0|0
+8|0.12410484181|0.579|0|0|0
+9|0.124156942427|0.579|0|0|0
+10|0.124211117879|0.579|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.150555812597|0.55|0|0|0
+2|0.142311920874|0.55|0|0|0
+3|0.136620218471|0.55|0|0|0
+4|0.133332525082|0.55|0|0|0
+5|0.131608594321|0.55|0|0|0
+6|0.130735595852|0.55|0|0|0
+7|0.130294553764|0.55|0|0|0
+8|0.130069818463|0.55|0|0|0
+9|0.129955085322|0.55|0|0|0
+10|0.129897934154|0.55|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.548665297741|0|0|0
+2|nan|0.548665297741|0|0|0
+3|nan|0.548665297741|0|0|0
+4|nan|0.548665297741|0|0|0
+5|nan|0.548665297741|0|0|0
+6|nan|0.548665297741|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.132268971939|0.536852063103|0|0|0
+2|0.127221430943|0.536852063103|0|0|0
+3|0.0402869192958|0.90296597201|0|0|0
+4|0.013252615212|0.971286274728|0|0|0
+5|0.00871043029935|0.978256030717|0|0|0
+6|0.00715632640869|0.9796193762|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.131039403329|0.544940222032|0|0|0
+2|0.0857365173791|0.654608774552|0|0|0
+3|0.0308658724279|0.924130017079|0|0|0
+4|0.0121295026073|0.97045794193|0|0|0
+5|0.00885947539087|0.976542485056|0|0|0
+6|0.00754022111713|0.978730785653|0|0|0

LSTMresults/Dataset0.LSTM_2C

@@ -0,0 +1,60 @@
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.14650396882|0.581|0|0|0
+2|0.134933630332|0.581|0|0|0
+3|0.12807858073|0.581|0|0|0
+4|0.125045746896|0.581|0|0|0
+5|0.123950693227|0.581|0|0|0
+6|0.123648752145|0.581|0|0|0
+7|0.123634647191|0.581|0|0|0
+8|0.123708837941|0.581|0|0|0
+9|0.123797330961|0.581|0|0|0
+10|0.123875424441|0.581|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.146884530544|0.579|0|0|0
+2|0.135687147945|0.579|0|0|0
+3|0.128945879199|0.579|0|0|0
+4|0.125824560031|0.579|0|0|0
+5|0.124595511615|0.579|0|0|0
+6|0.124180880323|0.579|0|0|0
+7|0.124085704312|0.579|0|0|0
+8|0.12410484181|0.579|0|0|0
+9|0.124156942427|0.579|0|0|0
+10|0.124211117879|0.579|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.150555812597|0.55|0|0|0
+2|0.142311920874|0.55|0|0|0
+3|0.136620218471|0.55|0|0|0
+4|0.133332525082|0.55|0|0|0
+5|0.131608594321|0.55|0|0|0
+6|0.130735595852|0.55|0|0|0
+7|0.130294553764|0.55|0|0|0
+8|0.130069818463|0.55|0|0|0
+9|0.129955085322|0.55|0|0|0
+10|0.129897934154|0.55|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.548665297741|0|0|0
+2|nan|0.548665297741|0|0|0
+3|nan|0.548665297741|0|0|0
+4|nan|0.548665297741|0|0|0
+5|nan|0.548665297741|0|0|0
+6|nan|0.548665297741|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.132268971939|0.536852063103|0|0|0
+2|0.127221430943|0.536852063103|0|0|0
+3|0.0402869192958|0.90296597201|0|0|0
+4|0.013252615212|0.971286274728|0|0|0
+5|0.00871043029935|0.978256030717|0|0|0
+6|0.00715632640869|0.9796193762|0|0|0
+LSTM_2C Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.131039403329|0.544940222032|0|0|0
+2|0.0857365173791|0.654608774552|0|0|0
+3|0.0308658724279|0.924130017079|0|0|0
+4|0.0121295026073|0.97045794193|0|0|0
+5|0.00885947539087|0.976542485056|0|0|0
+6|0.00754022111713|0.978730785653|0|0|0

RNNHandler.py

@@ -44,7 +44,7 @@ class RNNHandler:
 	results_directory = './RNNresults'
 	models_directory = './RNNmodels'
 
-	def __init__(self, model_name, num_categories, loss, optimizer):
+	def __init__(self, model_name, num_categories, loss, optimizer, optimizer_name):
 		# GET THE MODEL
 		fp_model = open(os.path.join(self.models_directory, model_name + '.json'), 'r')
 		model_str = fp_model.read()
@@ -56,11 +56,12 @@ def __init__(self, model_name, num_categories, loss, optimizer):
 		self.num_categories = num_categories
 		self.loss = loss
 		self.optimizer = optimizer
+		self.optimizer_name = optimizer_name
 
 
 	def fit_and_eval(self, x_train, y_train, x_test, y_test, nb_epoch, dataset_name):	#batch_size is always 1 and shuffle is always False, so we don't pass them as parameters
 		self.results_file = os.path.join(self.results_directory, dataset_name + '.' + self.model_name)
-		self.write_result(self.model_name + ' ' + dataset_name + ' Loss:' + self.loss + ' Optimizer:' + self.optimizer + ' Dropout:No')
+		self.write_result(self.model_name + ' ' + dataset_name + ' Loss:' + self.loss + ' Optimizer:' + self.optimizer_name + ' Dropout:No')
 		self.write_result('Epoch|Loss|Accuracy|Precision|Recall|Fscore')		
 		res_loss = []
 		res_accuracy = []

RNN_2.2C.py

@@ -0,0 +1,27 @@
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.vis_utils import plot_model
+import os
+import numpy as np
+from reader import Reader
+
+
+length = Reader.getInputShape()
+
+model = Sequential()
+
+#EXPECTS INPUT AS (nb_sample, timesteps, nb_features), where nb_sample=1 (batch_size = 1), timesteps = 1 and nb_features = length
+
+#model.add(Dense(40, input_dim = 12, init='uniform', activation='relu'))
+model.add(SimpleRNN(units=50, input_shape=(1,length), batch_input_shape=(1,1,length), recurrent_initializer='random_uniform', kernel_initializer='random_uniform', activation='sigmoid', return_sequences=True, stateful=True))
+model.add(SimpleRNN(units=50, recurrent_initializer='random_uniform', kernel_initializer='random_uniform', activation='sigmoid', stateful=True))
+model.add(Dense(5, kernel_initializer='random_uniform', activation = 'softmax'))
+
+
+model.summary()
+plot_model(model, to_file='./RNNmodels/RNN_2.2C.png', show_shapes=True)
+fp = open('./RNNmodels/RNN_2.2C.json', 'w')
+fp.write(model.to_json())
+fp.close()
+

RNN_plot_all.py

@@ -1,4 +1,5 @@
 import keras.utils
+from keras import optimizers
 from keras.models import Sequential, model_from_json, load_model
 from keras.layers import Dense, Activation, SimpleRNN
 from keras.utils.vis_utils import plot_model
@@ -27,7 +28,7 @@
 num_classes = 5
 #RNN_name = 'RNN_1A'
 
-num_epochs = 20
+num_epochs = 10
 
 (x_train, y_train), (x_test, y_test) = Reader.getDataset(dataset_id)
 #x_train = x_train[0:1000,:]
@@ -37,14 +38,18 @@
 
 for RNN_name in ['RNN_2C']:
 	results = {}
-	for loss,optimizer in [('mse','sgd')]:#, ('categorical_crossentropy','rmsprop')]:	#categorical_crossentropy
+	#for loss,optimizer in [('mse','sgd')]:#, ('categorical_crossentropy','rmsprop')]:	#categorical_crossentropy
 	#for optimizer in ['sgd', 'rmsprop']:
-		RNNmodel = RNNHandler.RNNHandler(RNN_name, num_classes, loss, optimizer)
-		(res_loss, res_accuracy, res_precision, res_recall, res_fscore) = RNNmodel.fit_and_eval(x_train, y_train, x_test, y_test, num_epochs, dataset_name)
-		if num_classes == 2:		
-			results[loss + '|' + optimizer] = res_fscore
-		else:
-			results[loss + '|' + optimizer] = res_accuracy
+	loss = 'mse'
+	optimizer = 'sgd'
+	#optimizer_w_params = optimizers.RMSprop(lr=0.00007)
+	optimizer_w_params = 'sgd'
+	RNNmodel = RNNHandler.RNNHandler(RNN_name, num_classes, loss, optimizer_w_params, optimizer)
+	(res_loss, res_accuracy, res_precision, res_recall, res_fscore) = RNNmodel.fit_and_eval(x_train, y_train, x_test, y_test, num_epochs, dataset_name)
+	if num_classes == 2:		
+		results[loss + '|' + optimizer] = res_fscore
+	else:
+		results[loss + '|' + optimizer] = res_accuracy
 
 	title = dataset_name + '.' + RNN_name
 	metric = 'accuracy'

RNNmodels/RNN_2.2C.json

@@ -0,0 +1 @@
+{"config": [{"config": {"kernel_regularizer": null, "units": 50, "bias_regularizer": null, "batch_input_shape": [1, 1, 79], "bias_constraint": null, "recurrent_regularizer": null, "stateful": true, "dtype": "float32", "recurrent_dropout": 0.0, "implementation": 0, "recurrent_constraint": null, "use_bias": true, "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}, "return_sequences": true, "activity_regularizer": null, "name": "simple_rnn_1", "unroll": false, "activation": "sigmoid", "trainable": true, "go_backwards": false, "kernel_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "recurrent_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}}, "class_name": "SimpleRNN"}, {"config": {"kernel_regularizer": null, "units": 50, "bias_regularizer": null, "bias_constraint": null, "recurrent_regularizer": null, "stateful": true, "recurrent_dropout": 0.0, "implementation": 0, "recurrent_constraint": null, "use_bias": true, "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}, "return_sequences": false, "activity_regularizer": null, "name": "simple_rnn_2", "unroll": false, "activation": "sigmoid", "trainable": true, "go_backwards": false, "kernel_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "recurrent_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}}, "class_name": "SimpleRNN"}, {"config": {"kernel_regularizer": null, "units": 5, "bias_regularizer": null, "bias_constraint": null, "name": "dense_1", "activity_regularizer": null, "activation": "softmax", "trainable": true, "kernel_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "use_bias": true, "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}}, "class_name": "Dense"}], "class_name": "Sequential", "keras_version": "2.0.3", "backend": "theano"}

RNNmodels/RNN_2C.json

@@ -1 +1 @@
-{"keras_version": "2.0.3", "config": [{"config": {"stateful": true, "trainable": true, "bias_regularizer": null, "recurrent_regularizer": null, "return_sequences": true, "use_bias": true, "name": "simple_rnn_1", "activity_regularizer": null, "unroll": false, "dtype": "float32", "activation": "relu", "batch_input_shape": [1, 1, 79], "units": 50, "bias_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "kernel_regularizer": null, "recurrent_initializer": {"config": {"maxval": 0.05, "minval": -0.05, "seed": null}, "class_name": "RandomUniform"}, "go_backwards": false, "recurrent_constraint": null, "recurrent_dropout": 0.0, "implementation": 0, "dropout": 0.0, "kernel_initializer": {"config": {"maxval": 0.05, "minval": -0.05, "seed": null}, "class_name": "RandomUniform"}, "kernel_constraint": null}, "class_name": "SimpleRNN"}, {"config": {"stateful": true, "trainable": true, "bias_regularizer": null, "recurrent_regularizer": null, "return_sequences": false, "use_bias": true, "name": "simple_rnn_2", "activity_regularizer": null, "unroll": false, "activation": "relu", "units": 50, "bias_constraint": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "kernel_regularizer": null, "recurrent_initializer": {"config": {"maxval": 0.05, "minval": -0.05, "seed": null}, "class_name": "RandomUniform"}, "go_backwards": false, "recurrent_constraint": null, "recurrent_dropout": 0.0, "implementation": 0, "dropout": 0.0, "kernel_initializer": {"config": {"maxval": 0.05, "minval": -0.05, "seed": null}, "class_name": "RandomUniform"}, "kernel_constraint": null}, "class_name": "SimpleRNN"}, {"config": {"bias_constraint": null, "name": "dense_1", "trainable": true, "bias_regularizer": null, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "use_bias": true, "units": 5, "activity_regularizer": null, "kernel_regularizer": null, "kernel_initializer": {"config": {"maxval": 0.05, "minval": -0.05, "seed": null}, "class_name": "RandomUniform"}, "activation": "softmax", "kernel_constraint": null}, "class_name": "Dense"}], "backend": "theano", "class_name": "Sequential"}
+{"keras_version": "2.0.3", "config": [{"config": {"return_sequences": true, "use_bias": true, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "units": 50, "dropout": 0.0, "batch_input_shape": [1, 1, 79], "bias_initializer": {"config": {}, "class_name": "Zeros"}, "kernel_regularizer": null, "unroll": false, "recurrent_constraint": null, "name": "simple_rnn_1", "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}, "implementation": 0, "recurrent_dropout": 0.0, "activation": "relu", "dtype": "float32", "stateful": true, "recurrent_regularizer": null, "bias_constraint": null, "trainable": true, "go_backwards": false, "recurrent_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}}, "class_name": "SimpleRNN"}, {"config": {"return_sequences": false, "use_bias": true, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "units": 50, "dropout": 0.0, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "kernel_regularizer": null, "unroll": false, "recurrent_constraint": null, "name": "simple_rnn_2", "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}, "implementation": 0, "recurrent_dropout": 0.0, "activation": "relu", "stateful": true, "recurrent_regularizer": null, "bias_constraint": null, "trainable": true, "go_backwards": false, "recurrent_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}}, "class_name": "SimpleRNN"}, {"config": {"activity_regularizer": null, "use_bias": true, "bias_regularizer": null, "kernel_constraint": null, "units": 5, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "kernel_regularizer": null, "bias_constraint": null, "trainable": true, "name": "dense_1", "kernel_initializer": {"config": {"minval": -0.05, "maxval": 0.05, "seed": null}, "class_name": "RandomUniform"}, "activation": "softmax"}, "class_name": "Dense"}], "backend": "theano", "class_name": "Sequential"}