VizConsoleAdded

fangde · Aug 9, 2017 · 0e4bfbd · 0e4bfbd
1 parent 8ad8fa2
commit 0e4bfbd
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Showing 10 changed files with 166 additions and 145 deletions.
diff --git a/examples/MnistDB.py b/examples/MnistDB.py
@@ -114,7 +114,7 @@ def find_data(self,args={'type':"train",'id':{"$lt":500}}):
         print "training reading"
         print t6
 
-        return d,l
+        return d.astype(np.float32),l.astype(np.int32)
 
     def generator_data(self,batch_size=20,args={'type':"train",'id':{"$lt":500}}):
 

diff --git a/examples/VizConsole.py b/examples/VizConsole.py
@@ -0,0 +1,104 @@
+import dash
+from dash.dependencies import Input,Output
+import dash_core_components as dcc
+import dash_html_components as html
+import plotly.graph_objs as go
+import pandas as pd
+import pymongo
+import seed
+
+db=seed.db
+
+
+
+
+
+
+
+df=pd.DataFrame(db.queryValLog({}))
+
+
+
+
+print df.columns.values.tolist()
+
+print df.groupby('studyID')
+
+
+print df.studyID.unique()
+
+
+app=dash.Dash()
+
+
+
+
+
+app.layout=html.Div(children=[
+    html.H1(children="hello dash"),
+
+    html.Div(children='''
+    Dash: a Weba pplication framework for python
+    '''),
+
+    dcc.Input(id='my-id', value='initial value', type="text"),
+    html.P(),
+
+    html.Div(id='my-div'),
+
+
+##for date visuzlaiton, server side rendering#
+##    html.Img(id='my-img',src="data:image/gif;base64,R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7"),
+
+
+    html.P(children="SelecViz"),
+
+    dcc.Dropdown(
+        options=[
+            {'label': 'New York City', 'value': 'NYC'},
+            {'label': u'Montreal', 'value': 'MTL'},
+            {'label': 'San Francisco', 'value': 'SF'}
+        ],
+        value='MTL',
+        multi=True
+
+    ),
+
+    dcc.Graph(id='example-graph',
+              figure={
+                    'data':[
+                        go.Scatter(
+                            x=df[df['studyID'] == i]['epoch'],
+                            y=df[df['studyID'] == i]['acc'],
+                            text=df[df['studyID'] == i]['time'],
+                            mode='markers',
+                            opacity=0.7,
+                            marker={
+                                'size': 10,
+                                'line': {'width': 0.5, 'color': 'white'}
+                            },
+                            name=i
+                        ) for i in df.studyID.unique()
+                    ],
+                  'layout':{
+                      'title':'Dash visulization'
+                  }
+
+
+              }
+
+    )
+
+])
+
+
+@app.callback(
+    Output(component_id='my-div',   component_property='children'),
+    [Input(component_id='my-id',    component_property='value')]
+)
+def update_output_div(input_value):
+    print "server call back"
+    return "{}".format(input_value)
+
+if __name__ =='__main__':
+    app.run_server(debug=True)
diff --git a/examples/__init__.py b/examples/__init__.py
diff --git a/examples/mnistDemo.py b/examples/mnistDemo.py
@@ -11,17 +11,26 @@
 mndb=seed.mn
 db=seed.db
 
+d,  l = mndb.find_data({'type': 'train'})
+dv,lv = mndb.find_data({'type':'val'})
+
+
 
 def run_one(name_test):
-    global d, l, c, f, fn
-    d, l = mndb.find_data({'type': 'train'})
+    print d.shape
     c, f, fn = db.load_model_architecture({'name': 'mlp'})
     db.studyID =name_test
     m1 = Model(fn, name_test, False)
-    m1.fit(10, d, l, 128, [DBLogger(db, m1)])
+    m1.fit(100, dv, lv, 128, [DBLogger(db, m1)],dv,lv)
+
+
+
+
+if __name__ == "__main__":
+    run_one('run3')
+    run_one('run4')
 
 
-run_one()
 
 
 

diff --git a/examples/seed.py b/examples/seed.py
@@ -37,19 +37,24 @@ def add_model():
 
 def init_data():
     X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
+
     X_train = np.asarray(X_train, dtype=np.float32)
     y_train = np.asarray(y_train, dtype=np.int32)
+
     X_val = np.asarray(X_val, dtype=np.float32)
     y_val = np.asarray(y_val, dtype=np.int32)
+
     X_test = np.asarray(X_test, dtype=np.float32)
     y_test = np.asarray(y_test, dtype=np.int32)
+
     print('X_train.shape', X_train.shape)
     print('y_train.shape', y_train.shape)
     print('X_val.shape', X_val.shape)
     print('y_val.shape', y_val.shape)
     print('X_test.shape', X_test.shape)
     print('y_test.shape', y_test.shape)
     print('X %s   y %s' % (X_test.dtype, y_test.dtype))
+
     mn.import_data(X_train, y_train, {'type': 'train'})
     mn.import_data(X_val, y_val, {'type': 'val'})
     mn.import_data(X_test, y_test, {'type': 'test'})

diff --git a/mytest.py b/mytest.py
@@ -85,7 +85,8 @@ def build_network(name,reuse=False):
 
     #n1.Params=w
 
-    n1.fit(5,d,l,128,[DBLogger(p2,n1)])
+    n1.fit(5,d,l,128,[DBLogger(p2,n1)],d[0:128],l[0:128])
+
 
     #p.del_params({})
     #p.del_train_log({})

diff --git a/tensorlab/Logger.py b/tensorlab/Logger.py
@@ -33,6 +33,7 @@ def on_epoch_end(self, epoch, logs={}):
         w=self.model.Params
         fid=self.db.save_params(w,logs)
         logs.update({'params':fid})
+
         self.db.valid_log(logs)
     def on_batch_begin(self, batch,logs={}):
         self.t=time.time()

diff --git a/tensorlab/Model.py b/tensorlab/Model.py
@@ -69,7 +69,8 @@ def fit(self,n_epoch, X_train, y_train,batch_size, callback=[],X_val=None,y_val=
                 c.on_epoch_begin(epoch,{})
             w=0
 
-            for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train,batch_size, shuffle=True):
+            for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
+
 
                 for c in callback:
                     c.on_batch_begin(w)
@@ -87,9 +88,8 @@ def fit(self,n_epoch, X_train, y_train,batch_size, callback=[],X_val=None,y_val=
             val_args={}
             if X_val is not None:
 
-                feed_dict = {self.x: X_val, self.y_: y_val}
-                [dc]=self.sess.run([self.loss],feed_dict=feed_dict)
-                val_args.update({'acc':dc})
+                vc=self.validate(X_val,y_val)
+                val_args.update({'acc':vc})
 
             for c in callback:
                 c.on_epoch_end(epoch,val_args)
@@ -159,6 +159,7 @@ def inference(self, X):
 
     def validate(self, X,Y):
         feed_dict={self.x:X,self.y_:Y}
+        feed_dict.update(self.network.all_drop)  # enable dropout or dropconnect layers
         [lo]=self.sess.run([self.loss],feed_dict=feed_dict)
         return lo