I want to train my data with a convolution neural network, I have reshaped my data: Those are parameters that I have used:
'x_train.shape'=(500000, 3253)
'y_train.shape', (500000,)
'y_test.shape', (20000,)
'y_train[0]', 97
'y_test[0]', 99
'y_train.shape', (500000, 256)
'y_test.shape', (20000, 256)
This is how I define my model architecture:
# 3. Define model architecture
model = Sequential()
model.add(Conv1D(64, 8, strides=1, padding='valid',
dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform',
bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None, kernel_constraint=None, bias_constraint=None, input_shape=x_train.shape))
# input_traces=N_Features
# input_shape=(batch_size, trace_lenght,num_of_channels)
model.add(MaxPooling1D(pool_size=2,strides=None, padding='valid'))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(x_train, y_train, batch_size=100, epochs=500,verbose=2)
But i got two Errors : 1-
ValueError: Error when checking input: expected conv1d_1_input to have shape (None, 500000, 3253) but got array with shape (500000, 3253, 1)
2-
With model.fit()
How do I resolve this problem?