Sachin Pal Sachin-crypto

## loading_model.py
my_model = load_model("D:/SACHIN/Models/Hand-Sign-Digit-Language/digit_model.h5")

## save_model.py
# Checking if the model exist otherwise save the model
if os.path.isfile("D:/SACHIN/Models/Hand-Sign-Digit-Language/digit_model.h5") is False:
    model.save("D:/SACHIN/Models/Hand-Sign-Digit-Language/digit_model.h5")

## fitting_data.py
# Running 10 epochs
model.fit(x=train_batches, validation_data=valid_batches, epochs=10, verbose=2)

## compiling_model.py
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])

## fine-tuned_model_summary.py
model.summary()

## no-trainable_layers.py
# We are not going to train last 23 layers and it's an arbitrary number
for layer in mobile.layers[:-23]:
    layer.trainable=False

## fine-tuned_model.py
model = Model(inputs=mobile.input, outputs=output)

## adding_output_layer.py
# Removing last 6 layers and adding our output layer
x = mobile.layers[-6].output
output = Dense(units=10, activation='softmax')(x)

## mobilenet_model_summary.py
mobile.summary()

## mobilenet_model.py
mobile = tf.keras.applications.mobilenet.MobileNet()
	# Checking if the model exist otherwise save the model
	if os.path.isfile("D:/SACHIN/Models/Hand-Sign-Digit-Language/digit_model.h5") is False:
	model.save("D:/SACHIN/Models/Hand-Sign-Digit-Language/digit_model.h5")
	# Running 10 epochs
	model.fit(x=train_batches, validation_data=valid_batches, epochs=10, verbose=2)
	# We are not going to train last 23 layers and it's an arbitrary number
	for layer in mobile.layers[:-23]:
	layer.trainable=False
	# Removing last 6 layers and adding our output layer
	x = mobile.layers[-6].output
	output = Dense(units=10, activation='softmax')(x)