model change

api/run.py

@@ -60,10 +60,10 @@ batch_size = 5
 epochs = 10
 accuracy=0
 execute_time=0
-layer_1_units=100
-layer_2_units=50
-layer_3_units=50
-dense_units=1
+input_units=100
+hidden_layer_1=50
+hidden_layer_2=25
+output_units=1
 
 # Scaler 
 scaler =  MinMaxScaler()
@@ -134,14 +134,11 @@ def input_and_targert(data,feature_length):
       x_samples.append(x_sample)
       y_samples.append(y_sample)
 
-  # Reshape the input as a 3D (Number of samles,length of features,features)
-
-  #Reshape input
+  # Reshape the input as a 3D (Number of Samples,time steps,features)
   X = np.array(x_samples)
   X=X.reshape(X.shape[0],X.shape[1],1)
   print("\n____Input Data Shape :____")
   print(X.shape)
-
   # Reshape Target 
   Y=np.array(y_samples)
   Y=Y.reshape(Y.shape[0],1)
@@ -257,23 +254,24 @@ def setup():
 
 
   #Add First LSTM Layer
-
-  model.add(LSTM(units = layer_1_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+  model.add(LSTM(units = input_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
 
   # Adding the  Second hidden layer and the LSTM layer
-
-  model.add(LSTM(units = layer_2_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+  model.add(LSTM(units = hidden_layer_1, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
 
   # Adding the  Third hidden layer and the LSTM layer
-  model.add(LSTM(units = layer_3_units, activation = 'relu', return_sequences=False ))
-
+  model.add(LSTM(units = hidden_layer_2, activation = 'relu', return_sequences=False ))
 
   # Adding the output layer
-  model.add(Dense(units = dense_units))
-
+  model.add(Dense(units = output_units))
   # Compiling model
   model.compile(optimizer = 'adam', loss = 'mean_squared_error')
 
+  print(model.input)
+  print(model.output)
+
+  print(model.summary())
+
 
   # Measuring the time taken by the model to train
   start_time=time.time()