Tensorflow results across platforms
0
I am experimenting on a two layered network to solve a regression problem (2 Input/ 1 Output). Results from Google's Colab and from Respberry Pi are way too different. It's just the exact code and same train & test data points.
Is it natural to be different or am I missing something?
tensorflow raspberry-pi google-colaboratory
asked Dec 29 '18 at 18:01
AnandAnand
724
add a comment |
0
I am experimenting on a two layered network to solve a regression problem (2 Input/ 1 Output). Results from Google's Colab and from Respberry Pi are way too different. It's just the exact code and same train & test data points.
Is it natural to be different or am I missing something?
tensorflow raspberry-pi google-colaboratory
asked Dec 29 '18 at 18:01
AnandAnand
724
1
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28
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I am experimenting on a two layered network to solve a regression problem (2 Input/ 1 Output). Results from Google's Colab and from Respberry Pi are way too different. It's just the exact code and same train & test data points.
Is it natural to be different or am I missing something?
tensorflow raspberry-pi google-colaboratory
asked Dec 29 '18 at 18:01
AnandAnand
724
I am experimenting on a two layered network to solve a regression problem (2 Input/ 1 Output). Results from Google's Colab and from Respberry Pi are way too different. It's just the exact code and same train & test data points.
Is it natural to be different or am I missing something?
tensorflow raspberry-pi google-colaboratory
tensorflow raspberry-pi google-colaboratory
asked Dec 29 '18 at 18:01
AnandAnand
724
asked Dec 29 '18 at 18:01
AnandAnand
724
asked Dec 29 '18 at 18:01
AnandAnand
724
asked Dec 29 '18 at 18:01
AnandAnand
724
asked Dec 29 '18 at 18:01
AnandAnand
724
724
1
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28
add a comment |
1
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28
1
1
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28
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1
Can you share a notebook showing your code? Also, do describe the difference you observe, and how you're training things on the Raspberry Pi.
– Bob Smith
Dec 29 '18 at 19:25
model = Sequential() model.add(Dense(10, input_dim=2, kernel_initializer='uniform', activation='relu')) model.add(Dense(40, kernel_initializer='uniform', activation='relu')) model.add(Dense(10, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='uniform')) adam = optimizers.Adam(lr=0.0001) model.compile(loss= 'mse' ,optimizer=adam, metrics=['mae']) model.fit(train_x, train_y, epochs=800, batch_size=16)
– Anand
Jan 2 at 17:28