Update README.md

README.md

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 # Object_Classification_Deep_Residual_Seperable_CNN_with_base_model_VGG16
+Object Classification using Resnet50 Network on cifar10 datasets from keras.  
+The Network Used is Resnet50 as the base model.  
+The images were imported from keras.datsets containing about 50000 training imaages with 10000 testing images.  
+The model was created with VGG16 as its base model and a Residual Seperable Convolutional layer following it.  
+The optimzer was Adam, 50 epochs with batch size of 16 and early stopping with patience of 10 and lr_reduce with factor of 0.1 and     patience of 2 were applied to the network.  
+The training set was split into validation set and tgraining set itself with a validation split = 0.1  
+The link of the Model -https://drive.google.com/open?id=1KOcHe3aDXZqz6UHUAdks5Y9WtaObB9Nc
+# The code explanation.
+The first Cell Includes all the import for the creation of the model.  
+It also includes the import of the cifar10 datsets from keras.datsets.  
+In the next cell I load the data into training and testing sets for the model creation and training.  
+def residual_block(mod_, f_in, f_out, strides_ = (1,1), use_shortcut_ = False):   \n",  
+    "    shortcut_ = mod_\n",  
+    "    \n",  
+    "    mod_ = SeparableConv2D(f_in, (1,1), strides=(1,1), padding = \"same\")(mod_)\n",  
+    "    mod_ = BatchNormalization()(mod_)\n",  
+    "    mod_ = LeakyReLU()(mod_)\n",  
+    "    if use_shortcut_ == True or strides_ != (1,1):\n",  
+    "        shortcut_ = SeparableConv2D(f_out, (1,1), strides=strides_, padding = \"same\")(shortcut_)\n",  
+    "        shortcut_ = BatchNormalization()(shortcut_)\n",  
+    "        \n",  
+    "    mod_ = Add()([shortcut_, mod_])\n",  
+    "    mod_ = ReLU()(mod_)\n",  
+    "    \n",  
+    "    return mod_"  
+ The above code is for Generating the Residual block used after the VGG16 base model represented as follows:  
+ "def model_build():\n",  
+ "    base_model = VGG16(weights = None, include_top = False)\n",  
+    "    x = base_model.output\n",  
+    "    x = residual_block(x, 16,16, use_shortcut_ = True)\n",  
+    "    x = GlobalAveragePooling2D()(x)\n",  
+    "    pred = Dense(10, activation = \"softmax\")(x)\n",  
+    "    model_imagenet = Model(input=base_model.input, output = pred)\n",  
+    "    return model_imagenet"  
+ This is the model that we trained on the cifar datasets. 
+ As one can see the base model is the predefined renet50 model pretrained with imagenet dataset.
+# The Dataset is as follows
+Datasets - from keras.datasets import cifar10  
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()  
+# The model Summary
+"model_imagenet=model_build()\n",  
+"model_imagenet.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",  
+"model_imagenet.summary()"    
+This gives us the model summary containing 23,608,202 parameters with 23,555,082 as trainable parameters and rest as non trainable parameters.  
+![Summary](Capture.PNG)
+# The Model Image
+![Model](dataset-cover.png)
+# Training Model
+"resnet_val_hist = model_resnet.fit(X_train, y_train, epochs = nepoch, batch_size=batch_size, callbacks = [lr_reduce, checkpoint, earlyStopping], validation_split = 0.1, verbose = 2)"  
+The batch size is 32  and the epochs are 100 with early stopping of  patience 10.  
+# Results
+The Training accuracy was 95.38.  
+The testing acuuracy was 93.12