Update app.py

app.py

@@ -1,4 +1,75 @@
 import streamlit as st 
 import numpy as np 
-from sklearn.datasets import make_circles,make_moons
-from tensorflow import keras
+from sklearn.datasets import make_circles,make_moons,make_blobs
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from tensorflow import keras
+import matplotlib.pyplot as plt 
+import seaborn as sns 
+from keras.models import Sequential
+from keras.layers import InputLayer,Dense,Dropout 
+from keras.losses import MeanAbsoluteError,MeanSquaredError
+from keras.optimizers import SGD
+from keras.regularizers import l1,l1,l1_l2
+
+from mlxtend.plotting import plot_decision_regions
+
+st.title("TensorFlow Playground")
+
+with st.sidebar:
+    st.header("Choose Dataset")
+    dataset = st.selectbox("Select Dataset",["Blobs","Circles","Moons"])
+    # on = st.toggle("Upload Dataset(.csv file)")
+    # if on:
+    #     st.write("**Note:** Only 2 features are allowed.")
+    #      up_file = st.file_uploader("Upload Dataset (.csv or .xlsx)", type=["csv"])
+        
+    noise = st.slider("Noise",0.0,1.0,0.1)
+    test_size = st.slider("Test Size",0.1,0.5,0.05)
+
+    st.header("ANN Hyperparameters")
+    hl = st.number_input("Hidden Layers",0,10,1,value=2)
+    numbers = st.text_input("Neurons for each hidden layer" ,placeholder="provide comma seperated e.g. 8,16,32")
+    input_func = lambda x: [int(i.strip()) for i in x.split(",") if i.strip() != ""]
+    nn = input_func(numbers)
+    epochs=st.number_input("Epochs",1,10000,1,value=10)
+    
+
+    
+    col1, col2 = st.column(2)
+
+    with col1:
+        af = st.selectbox("Activation Function",["Sigmoid","Tanh","Relu"])
+    with col2:
+        lr = st.selectbox("Learning Rate".[0.1,0.01,0.02,0.2])
+
+    # col3,col4 = st.column(2)
+
+    # with col3:
+    reg = st.selectbox("Regularizer", ["None", "L1", "L2","ElasticNet"])
+    if reg != "None":
+        reg_rate = st.slider("Regularization rate", 0.0, 0.1, 0.01, 0.01)
+    # with col4:
+    es = st.selectbox("Early Stopping",["No","Yes"],index=0)
+    if es == "Yes":
+        col3, col4 = st.columns(2)
+        with col3:
+            min_delta = st.number_input(0.001,0.9,0.1,value=0.001)
+            )
+        with col4:
+            patience =  min_delta = st.number_input(3,20,0.1,value=3)
+            )
+    if st.sidebar.button("start trainning"):
+
+        
+
+        
+        
+        
+    
+
+    
+    
+    
+
+