nouffer/SumasenTrainer
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update cate size

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Mohamed Nouffer
2021-10-22 13:12:47 +05:30
parent 650353cb6a
commit f8ce682583
1 changed files with 8 additions and 6 deletions
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trainer.py
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@ -51,7 +51,7 @@ def tranferLearningVGG(model, train_dir, test_dir):
############ Model creation ################ ############ Model creation ################
def buildSimpleModel(): def buildSimpleModel(cats):
model = keras.Sequential() model = keras.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(100,100,3))) model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(100,100,3)))
model.add(layers.MaxPooling2D((2,2))) model.add(layers.MaxPooling2D((2,2)))
@ -65,7 +65,7 @@ def buildSimpleModel():
model.add(layers.Flatten()) model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(5, activation='softmax')) model.add(layers.Dense(cats, activation='softmax'))
return model return model
###### preapre iterater ######## ###### preapre iterater ########
@ -204,6 +204,7 @@ if training_mode == "Existing model":
if 'model_name' in st.session_state: if 'model_name' in st.session_state:
st.write('Selected models is :', st.session_state['model_name']) st.write('Selected models is :', st.session_state['model_name'])
st.subheader("Predict a image") st.subheader("Predict a image")
input_shape = st.selectbox('Select image input shape',[(100,100), [150, 150], (200,200), (240,240), (300, 300)])
file = st.file_uploader("Upload image",['jpeg', 'jpg', 'png']) file = st.file_uploader("Upload image",['jpeg', 'jpg', 'png'])
if file: if file:
bytes_data = file.getvalue() bytes_data = file.getvalue()
@ -211,12 +212,12 @@ if training_mode == "Existing model":
f.write(file.getbuffer()) f.write(file.getbuffer())
model_file = os.path.join(st.session_state['model_folder'], st.session_state['model_name']) model_file = os.path.join(st.session_state['model_folder'], st.session_state['model_name'])
mdl = load_model(model_file) mdl = load_model(model_file)
img = load_img(os.path.join(st.session_state['model_folder'], file.name), target_size=(100,100)) img = load_img(os.path.join(st.session_state['model_folder'], file.name), target_size=input_shape)
st.image(img) st.image(img)
img = img_to_array(img) img = img_to_array(img)
img = img.reshape(1,100,100,3) img = img.reshape(1,input_shape[0],input_shape[1],3)
res = mdl.predict(img) res = mdl.predict(img)
st.write(np.argmax(res)) st.markdown('## Category is {}'.format(np.argmax(res)))
#st.write([key for key in class_labels][np.argmax(res)]) #st.write([key for key in class_labels][np.argmax(res)])
else: else:
@ -246,7 +247,7 @@ else:
model_name = st.text_input('model name:', 'mymodel.h5') model_name = st.text_input('model name:', 'mymodel.h5')
epochs = st.slider("Epochs",1,500, 2) epochs = st.slider("Epochs",1,500, 2)
if st.button('begin train') and epochs and batch_size: if st.button('begin train') and epochs and batch_size:
mdl = buildSimpleModel() mdl = buildSimpleModel(len(class_labels))
text_output = st.empty() text_output = st.empty()
graph = st.empty() graph = st.empty()
my_bar = st.empty() my_bar = st.empty()
@ -254,6 +255,7 @@ else:
his_df = pd.DataFrame(history.history) his_df = pd.DataFrame(history.history)
st.line_chart(his_df) st.line_chart(his_df)
training_mode = "Existing model" training_mode = "Existing model"
st.markdown('## Training completed, plese check the output folder for saved model.')
except Exception as ex: except Exception as ex:
st.error(ex) st.error(ex)